A COMPUTATIONAL FRAMEWORK FOR USING BOTH RULES AND PREVIOUSLY-DECIDED CASES IN A LEGAL DECISION MAKING PROCESS
UCL
KAMALENDU PAL
Department of Computer Science University College London Gower Street, London WCIE 6BT
A thesis submitted for the degree of Master of Philosophy in the University of London
June 1997
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A hybrid knowledge-based system, Advisory Support for Home Settlement in Divorce
(ASHSD), which exploits both general legal rules (for rule-based reasoning or RBR) and specific information taken from previously-decided similar cases (for case-based reasoning or CBR), is described. Legal knowledge in the system covers three aspects of matrimonial home settlement in divorce in English law, namely owned home settlement, transfer of tenancy, and injunctions to protect a spouse, or a family member in the custody of a spouse. The user can select either reasoning method (RBR or CBR), or indicate no preference. ASHSD’s rule base consists of two types of rule. The first type of rules determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rules.
When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions.
When no preference is indicated, the system applies each method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. ACKNOWLEDGEMENT
It is a great pleasure to acknowledge my debt to many people involved, directly or indirectly, in the completion of this thesis. I am particularly grateful to my supervisors,
Professor John Campbell and Dr Mark Levene, for encouraging me in this work, offering suggestions and pruning many unfruitful branches of research. I am grateful to John for his guidance and especially for the time and effort he spent restructuring and making constructive suggestions on early drafts of this thesis.
I am grateful to Dr Stephen Guest of the Law Faculty, University College London, for taking time from his busy schedule in the validation study of the implemented system. I am also thankful to Dr Dan Hunter of the Law School, University of Melbourne, Australia, for providing useful comments on the implemented software.
It is now my pleasurable duty to record my indebtedness to some of my friends for the help they have given me in producing this thesis. Formost amongst them is Sukhdev Kheb- bal who, over last three years, has given me valuable information on software. Tony Bal- lardie, San jay Kadam, William Langdon, Jens Dzikowski, Rafael Sordini, Eliseo Reategui,
Wilfred Ng, Arif Iqbal and Jonathan Poole have provided me with their expert opinions and help.
Finally, thanks also go to all members of the Department of Computer Science at UCL for their kind help and cooperation. Contents
1 Introduction 1
1.1 B a c k g ro u n d ...... 1
1.2 Artificial Intelligence in Context of L a w ...... 2
1.3 The Legal Theoretical Concepts ...... 3
1.3.1 The Legal Positivists ...... 4
1.3.2 Legal Realism ...... 8
1.4 The Legal Decision-Making Process ...... 11
1.5 Model for the Present Research ...... 18
1.5.1 Identification and Acquisition of K n o w le d g e ...... 20
1.5.2 Representation of the Collected K now ledge ...... 23
1.5.3 Reasoning on the Represented Knowledge ...... 24
1.5.4 Notion of Rule-Based R easoning ...... 25
1.5.5 Notion of Case-Based Reasoning ...... 29
1.6 Subject Area of the Research...... 30 C on ten ts iii
1.6.1 Project Orientation ...... 31
1.6.2 Why a Hybrid System? ...... 32
1.6.3 Overview of A SH SD ...... 33
1.7 Structure of the Thesis...... 37
2 Previous Related Research 39
2.1 Introduction ...... 39
2.2 Rule-Based Reasoning System s ...... 40
2.2.1 TAXMAN I P roject ...... 40
2.2.2 B ritish N ationality A ct ...... 41
2.2.3 The Oxford Project ...... 45
2.2.4 LDS and SAL Systems ...... 46
2.2.5 TAXADVISOR ...... 47
2.2.6 Latent Damage System ...... 47
2.2.7 The Use of Meta-rules in Rule-Based Legal Computer Systems . . . 48
2.2.8 Commentary on RBR System s ...... 48
2.3 Case-Based Reasoning System s ...... 49
2.3.1 JUDGE ...... 49
2.3.2 H Y P O ...... 51
2.3.3 The Trademark Reasoning and Retrieval System (TR2S) ...... 52 Contents iv
2.3.4 CHASER ...... 53
2.3.5 Commentary on CBR System s ...... 54
2.4 Hybrid Legal Knowledge-Based System s ...... 55
2.4.1 TAXMAN I I ...... 55
2.4.2 Gardner’s System ...... 56
2.4.3 GREBE ...... 57
2.4.4 CABARET ...... 58
2.4.5 PROLEXS ...... 60
2.4.6 IKBALS I I ...... 61
2.4.7 H ELIC I I ...... 61
2.4.8 Conclusions ...... 62
3 Framework of the System 64
3.1 Introduction ...... 64
3.2 Concepts behind ASHSD ...... 65
3.2.1 Characterisation ...... 65
3.2.2 Objectives for the System ...... 65
3.3 ASHSD’s Knowledge Base Developm ent ...... 66
3.4 The Domain; Matrimonial-Home-Related O rders ...... 66
3.5 Methodology ...... 69 C on ten ts v
3.5.1 Model Building ...... 70
3.5.2 Knowledge Acquisition ...... 72
3.5.3 Incremental Development of Full System ...... 80
3.5.4 Testing and User T rials ...... 82
3.6 The ASHSD Hybrid Reasoning M odel ...... 83
3.6.1 System Control M echanism ...... 83
4 Knowledge Representation and Organisation 86
4.1 Introduction ...... 86
4.1.1 Legal Knowledge in Rule F o rm ...... 86
4.2 Legal Knowledge in Case F o rm ...... 88
4.2.1 ASHSD - Case Knowledge Representation and Organisation ...... 91
4.3 Example: Martin (B.H.) v Martin (D .) ...... 95
4.3.1 Judicial Opinion for Martin v Martin ...... 96
4.3.2 Knowledge Representation and Organisation for Martin v Martin case 99
4.3.3 Matrimonial-Home Hypernode Structure and Contents ...... 102
4.3.4 Appeal Hypernode of the Martin v Martin case ...... 103
4.3.5 Decision Hypernode of the Martin v Martin c a se ...... 103
4.3.6 Summary ...... 104
5 Reasoning With Imprecise Knowledge 105 C on ten ts vi
5.1 Knowledge-Based System and Fuzziness ...... 105
5.1.1 Overview of Fuzzy Set Theory ...... 106
5.1.2 What is Fuzziness ? ...... 106
5.2 Fuzzy Sets - Notation, Terminology and Basic Properties: ...... 108
5.2.1 Classical Set or Crisp S e t ...... 108
5.2.2 Characteristic Function of a Crisp S e t ...... 108
5.2.3 Fuzzy Set and its Characteristic Function ...... 109
5.2.4 Formal Fuzzy Set Representation ...... I l l
5.2.5 Hedges of Fuzzy S ets ...... 113
5.3 Fuzzy Knowledge Representation in ASHSD ...... 115
6 Organisation of Knowledge Base 119
6.1 Introduction ...... 119
6.2 Rule Base Structure ...... 120
6.2.1 Comprehensive Advice ...... 122
6.3 Case Base Management and Case R etrieval ...... 127
6.3.1 Example : Exclusion Order From a Rented Matrimonial Home . . . 130
6.3.2 Selection of Similar Cases for a New C a se ...... 135
6.3.3 Partial Advice Mechanism ...... 141
6.3.4 Example: Owned Matrimonial Home Settlement After Divorce . . . 149 Contents vii
6.3.5 Selection of Similar Cases for a New C a se ...... 149
6.3.6 No Rule-Based Advice ...... 157
6.3.7 No Suggestion from Rule Base and Case B ase ...... 157
6.4 Automated Decision on the Qualities of the Reasoning M ethods ...... 157
7 Empirical Observations and Validation of ASHSD 159
7.1 Introduction ...... 159
7.2 Why Empirical Observation ? ...... 160
7.2.1 Empirical Observations ...... 160
7.2.2 Example: The Overall Suitability of any Particular Reasoning Method 160
7.3 Conclusions ...... 174
7.4 Overview of the Project Validation ...... 175
7.4.1 Validation of ASHSD ...... 175
7.4.2 Second Step of Validation ...... 181
7.5 Knowledge Base Maintenance Aspects of ASHSD ...... 182
8 Conclusions 186
8.1 Introduction ...... 186
8.2 Motivation ...... 186
8.3 Summary of R esults ...... 187
8.4 Critical Inspection of A SH SD ...... 190 C on ten ts viii
8.5 Limitation and Future Research ...... 192
8.5.1 Integration of ASHSD with a Data Base Management System .... 192
8.5.2 Incorporation of a Better User Interface ...... 193
8.5.3 Inclusion of a Critic M odule ...... 193
8.5.4 Extension of the Scope of ASHSD ...... 194
8.6 Concluding Remark ...... 194
A Previously Decided Cases 195
B Questionnaire Used in the Validation Process 215
C Questionnaire Used in the Critical Inspection of ASHSD 218
C.l Background Information about the System ...... 219
C.2 Different Rule-Based Advice ...... 223
C.2.1 Case-Based Advice ...... 223
C.2.2 Questionnaire for the test cases ...... 224
C.3 Extra Questions ...... 227
Bibliography 229 List of Figures
1.1 The computational framework ...... 33
1.2 Different types of behaviour leading to rule-based output ...... 36
3.1 Decomposition of legal analysis block ...... 71
3.2 Decomposition of owned matrimonial home block ...... 72
3.3 Decomposition of offending behaviour ...... 74
3.4 An ‘available-action(s)’ rule for an injunction-related c a s e ...... 76
3.5 An ^available-action(s)^ rule for an owned home settlement c a se ...... 77
3.6 Rule-based ‘prediction’ in an exclusion order for protecting a spouse .... 78
3.7 Rule-based ‘prediction’ in an exclusion order for protecting a c h ild ...... 79
3.8 System layout ...... 84
4.1 The general rule structure and a tenancy transfer r u le ...... 87
4.2 Hypernode casebase ...... 91
4.3 A portion of the ‘participants’ hypernode ...... 92
IX List of Figures x
4.4 Different types of relationship hypernodes ...... 93
4.5 Different types of matrimonial home ...... 94
4.6 Different types of matrimonial home hypernodes ...... 95
4.7 The main case hypernode for the Martin v Martin case ...... 96
4.8 Index hypernodes for the Martin v Martin c a se ...... 97
4.9 Participants and relation hypernodes for the Martin v Martin c a s e ...... 97
4.10 Participant hypernodes for the Martin v Martin case ...... 98
4.11 Adult and children participant hypernodes for the Martin v Martin case . . 100
4.12 Legal and group participants for the Martin v Martin case ...... 101
4.13 Relations among the participants for the Martin v Martin ca s e ...... 101
4.14 Facts hypernode for the Martin v Martin case ...... 102
4.15 Different court hypernodes for the Martin v Martin case ...... 102
4.16 Matrimonial-home hypernode for the Martin v Martin c a s e ...... 102
4.17 Appeal hypernode for the Martin v Martin case ...... 103
4.18 Decision hypernode for the Martin v Martin c a se ...... 103
5.1 Characteristic function of A ...... 109
5.2 A characteristic function for fuzzy set G O O D ...... 109
5.3 Fuzzy sets on length of marriage with ‘very’ h ed g e ...... 114
5.4 The advice and the preconditions of BRU LEO l ...... 117 List of Figures xi
5.5 The advice and the preconditions of BRU LE19 ...... 118
6.1 rule bcLse s t r u c t u r e ...... 120
6.2 Representation of steps in the similarity cissessment ...... 128
6.3 Diagrammatic representation of the relative distances between cases .... 134
6.4 Classification of preconditions of BRU LE22 ...... 144
7.1 The comparison of RBR and CBR effectiveness on test cases ...... 172
7.2 The different decision categories in graphical form ...... 173
C.l The computational framework ...... 220
C.2 Different types of preconditions of BRULE112 ...... 221
C.3 Different types of behaviour leading to rule-based output ...... 222 List of Tables
5.1 Fuzzy term ‘good’ ...... 107
6.1 Some of the previously-decided injunction-related cases stored in the case
b a s e ...... 130
6.2 The relative similarity distance for injunction cases ...... 132
6.3 The mutual similarity coefficients of the above injunction-related cases . . . 136
6.4 Some of the previously-decided owned home settlement cases stored in the
case base ...... 150
6.5 The relative similarity distance for owned home settlement c a se s ...... 151
6.6 The mutual similarity coefficients for the above cases ...... 152
7.1 Real test cases used in the empirical observations ...... 162
7.2 Derived (hypothetical) test cases used in the empirical observations ...... 163
7.3 The scores and the appropriateness of categories for some test cases . . . .170
7.4 The results of the case base validation ...... 179
7.5 Grading structure to assess the case base validation ...... 180
X ll List of Tables 0
7.6 Validation of ASHSD by the legal expert ...... 182 Chapter 1
Introduction
1.1 Background
Computer science, and in particular Artificial Intelligence (AI), is now making a noticeable impression on law - as witness the growing number of journals and publications dealing with AI and law together. Furthermore, the legal domain has become an area in which applications of AI have grown substantially. However, the majority of the research on applications of AI for the legal profession and the consequent track record of research on legal reasoning have not yet produced eflfective support systems at a high level of legal competence. A typical opinion of the state of the art is:
Though computers are used extensively by lawyers for legal data storage and re
trieval purposes, they have not yet been satisfactorily programmed to interpret
this raw material or to provide assistance in reasoning with, drawing infer
ences from, and offering advice on the basis of, the formal sources of the law.
[(Susskind, 1987a), pp.3]
The shortcomings just mentioned still constitute a reasonable opinion in 1997. Chapter 1. Introduction 2
Many aspects of legal reasoning require further study before the situation can be improved qualitatively. In this thesis, our primary concern is computer science and not law. But even without detailed or specialised knowledge of legal practices, we can identify one aspect of legal reasoning which is also present for many other kinds of expertise: rules and cases are used, and sometimes together. In research on knowledge-based systems in general, using rules and cases together in treating some problem needs to be understood better. We examine one specific and new approach to this use of knowledge in two different representations, with examples of knowledge drawn from a specialised area of law.
1.2 Artificial Intelligence in Context of Law
The advance of information technology (IT) and its impact on our daily life cannot be un derestimated. However, legal professionals have been slow to exploit the immense potential of IT. Instead of using IT to maximise productivity and fee-earning capacity, computer systems have been used mainly for word processing and accounting within the legal pro fession. IT hcLS yet to be exploited as an effective tool by lawyers.
The most valuable resources of legal firms are the knowledge and expertise of lawyers.
But the knowledge of senior legal practitioners is not easily available to less experienced lawyers, students, and clients. Clearly, relevant technologies in this area are knowledge- based system (KBS): in particular, expert systems which would allow scarce expertise and knowledge to be more widely available and easily accessible. For lawyers and clients to have access to valuable expertise at the touch of a few keys could improve productivity, quality, and performance. In addition, the use of these technologies could give legal firms a competitive advantage over others that are slower to adopt the technology. Therefore the construction of automated legal décision-support systems is a valuable exercise, from the point of view of determining what is feasible, desirable and/or efficient. This view is not unusual to find among those involved in research and in the parts of legal practice where a sympathetic view of research exists.
In automating the legal décision-support systems, one has to understand enough of the nature of legal decision-making processes to avoid exaggerated claims and to be sure that Chapter 1. Introduction 3 what is delivered produces output that a legal specialist would regard as helpful and not irrelevant or misleading. This understanding includes theories and views about what law is, what are typical legal problems and how they are solved, what are the uses for legal knowledge-based systems, and so forth. Since most of these aspects refer in some way, even indirect, to the theoretical foundations of law as a discipline, legal theory should be considered for its relevance to the automation of any legal décision-support system.
In the next section, therefore, we discuss several ideas from classical authors in legal theory for their potential relevance to our work. However, this thesis is not intended as a contribution to legal theories; its significance is in computer science, in connection with a specific automated décision-support system that uses multiple knowledge representations.
1.3 The Legal Theoretical Concepts
Different researchers [e.g. (Susskind, 1987a), (Gardner, 1987), (Zeleznikow & Hunter,
1994)] of AI and law have argued for the relevance of jurisprudence^ to the automation of legal decision-making processes. Susskind writes:
It is beyond argument, however, that all expert systems must conform to some
jurisprudential theory because all expert systems in law necessarily make as
sumptions about the nature of the law and legal reasoning. To be more specific,
all expert systems must embody a theory of structure and individuation of laws,
a theory of legal norms, a theory of descriptive legal science, a theory of legal
reasoning, a theory of logic and the law, and a theory of legal systems, as well
as elements of a semantic theory, a sociology and a psychology of law (theories
that must all themselves rest on more basic philosophical functions). If this
is so, it would seem prudent that the general theory of law implicit in expert
systems should be explicitly articulated ... [(Susskind, 1987a), pp.20]
' Jurisprudence means legal philosophy (or legal theory). Among the topics included in legal philosophy are theories of law; the concepts of law and legal institutions; legal reasoning and adjudication; and many other related subject-areas. Chapter 1. Introduction 4
The above specification makes some demands that are unique to legal knowledge-based systems. In other areas (e.g. engineering, medical science, etc.) for which one would wish to design a knowledge-based system there is much more of a consensus about the nature of the theory that describes the discipline. An engineering or medical school would generally not conduct courses on the nature of engineering or medicine because the practice of these professions hardly ever raises the kind of foundational questions that drive one back to
basic theory about the discipline. Such concerns, however, often arise in legal practice;
hence, these are the kinds of question that a course in jurisprudence asks about law. In law there is no single agreement about what law is, nor about the true nature of legal
reasoning, but some agreement is nevertheless needed if legal rationality is to be self- conscious. Moreover, legal practitioners have no body of scientific knowledge lying at the
heart of their discipline, unlike experts in the fields where other knowledge-based systems
have been created successfully. Instead, law rests upon sets of fundamental values about which there is a great deal of debate.
To clarify the situation, we shall introduce some background to jurisprudence that is
relevant to our research. We focus on what is important to understand when we discuss
‘modelling’ law in the sense that is relevant for our own application. This includes ap
preciation of the two most influential camps in jurisprudence: the legal positivists and th e legal realists. These two camps dispute over one highly relevant question: is legal
decision-making constrained by rules ? We look first at the approach of legal positivists,
before examining the approaches of the legal realists.
1.3.1 The Legal Positivists
Legal positivism emerged in its modern dress in the work of Jeremy Bentham (Hart,
1982) and his follower John Austin (Morison, 1982). The central claim of legal positivism is that law is separate and distinct from morality. Positivists claim that law is based upon explicit rules and whether some rules are legal rules depend upon whether they have been laid down in some source such as legislation, codes, previously-decided case reports or doctrines. Moreover, the rules are discernible regardless of their origin. Chapter 1. Introduction 5
However, legal positivists do not deny that judges sometimes decide cases by reference to moral values or social policy considerations. It is necessary for the decision-maker to do this, according to the positivists, whenever the existing rules of law fail to give a clearly- determined answer in a specific case. In claiming that law is separate from morality, the
positivists are denying that moral judgments are necessary to discover what the existing law is; but discovering the existing law is not always enough in itself to decide a case.
Where the law does not give an answer, the decision-maker must establish, by his or her decision, a new legal rule^ and this will be done on the basis of extra-legal considerations of morality and social policy. Thus, the legal positivists seek to find a series of legal rules
which explain the various laws.
The main theme of rule-based jurisprudence is not a new one, though it has been
accepted and accommodated freely during this century. In the early part of this century
there was a school that presents an appealing example to those interested in the application of computer methods to law. A term ‘mechanical jurisprudence’^ which gives a picturesque description of it, appeared at least as early as 1908 with the publication of Dean Roscoe
Pound’s article (Pound, 1908).
The model of legal reasoning by Pound, which is based on a logical deduction, needs
to satisfy two separate criteria for this model to function. First, legal concepts must
be specific, well-founded, clear and inclusive of all cases. Secondly, these legal concepts
must be suitable for representation as either logical statements^ or production rules^ which
would typically be of the form: IF
deduction is the only form of reasoning needed, if the law can indeed be represented in such
a simple way. However, for proponents of such an approach, which we can call m echanical
^Formal logic \s used to represent the knowledge cuid prepositional calculus is usually the representation
scheme. For example: p & q ^ r (mecining that p and q together imply r) where p stands for ‘Tony
is drunk’, q means ‘Tony is driving his car’, and r indicates that ‘Tony’s driving licence must be revoked
by the transport authority’. Therefore this example represent the drink-driving legal rule in prepositional calculus.
^Production rules, i.e. rules of the same logical form but which are used to produce new information.
This involves basically a deductive reasoning process. For example, if the knowledge base of a system
contains the rule if A then B and the fact A is true, it adds B is true to the knowledge base. Chapter 1. Introduction 6 jurisprudence^ law is not so simple. Instead the modern legal theorists have proposed ways in which we can adapt the positivist theory to contemplate the real world more accurately.
The starting-point for the modern legal positivists is the work of H.L.A. Hart. His thesis [e.g. (Hart, 1961), (Hart, 1983)] is that law consists of rules only. These rules can
be classified into the primary rules and the secondary rules. Primary rules are rules about conduct, of the kind with which we are all familiar: ‘do not bully \ ‘do not steal’, ‘always
obey your parents etc. Secondary rules are rules about other rules: about how to alter
other rules, how to interpret them, how to enact them, and how to recognise them as valid
rules. When the meaning of the words of a rule makes it applicable to a given factual situation, he agrees to apply logical deduction. But each rule hets a ‘penumbra of doubt’,
where it is not known whether the rule applies or not. This leads to the notions of ‘easy
case ’ and ‘hard case ’.
The distinction between hard and easy cases is usual in the jargon of jurisprudence.
An easy case is one where the legal decision for the issue is clear and basically undisputed.
One generally takes into consideration that easy cases are settled whenever the facts are
clear. Easy cases that come the point of litigation do so only in order that questions of fact can be resolved according to the legal rules of proof. Once the court has established
the facts, the legal decision should then be simple and easily achieved by the use of the
law on the facts according to standard types of inference. Since easy cases by definition
do not raise difficult legal issues, they are hardly ever appealed.
Hard cases are an unavoidable feature of the law. A hard case is one that does not fall
under a settled rule of law, or that appears to fall under two rules, the use of which would
produce differing or opposing solutions, or is a case that falls clearly under one legal rule,
but where the outcome would be an irrational result. Since the possibilities for human
interaction are so varied and since we can never fully predict the future, it is impossible to forecast and create a useful rule which would anticipate and resolve every useful aspect of
human interaction. When the variations of life throw into the centre of the legal system
a unique kind of dispute, arising from a new kind of activity, a new technology, or a new
perspective which creates a case that fails to fall comfortably under the prevailing rules,
the law must still deal with it somehow within its existing conceptual structure, and hence Chapter 1. Introduction 7 we have a hard case for the law.
For a easy case the decision can be made deductively. But for cases belonging in the penumbra, i.e. hard cases, which cannot be decided in a deductive way. Hart believes that the decision-maker may well decide the case at his own discretion. As Hart says:
Where the rules are vague, all we can do is to predict what the judges will say
[(Hart, 1983), pp.168].
When the area of open texture"^ is reached very often all we can profitably offer
in answer to the question: ‘What is the law of the matter?’ is a guarded
prediction of what the courts will do. [(Hart, 1961), pp. 143].
One of the contemporary defenders of legal positivism is Richard Susskind, who has
advocated the use of legal knowledge sources that may reduce the vagueness of the original
law-statement when one is considering hard cases. Susskind claims:
Rules do and should play a central role in legal science, legal knowledge rep
resentation and legal reasoning. Overwhelming authority for this proposition
can be found in legal theory, and even a philosopher such as Dworkin, who has
questioned the sufficiency of rules for legal decision-making, does nevertheless
himself seem to presuppose a predominant place for them, as MacCormick has
shown. If this is so, and rules have, as MacCormick alleges, ‘logical primacy’,
then surely it should be sought, in the first instance, to represent legal knowledge
in rule form. [(Susskind, 1987a), pp.78-79]
Nevertheless, according to Susskind’s own analysis (Susskind, 1987) there is a large
diversity of opinions among legal theorists about what rules actually are. While they
seem to agree that rules have antecedents and consequents, there are many views about
‘Open texture’ is related to the vagueness of a term. Chapter 1. Introduction 8 what these are made of. To follow them up in detail would be beyond the scope of this thesis even if it were primarily about law and not about computer science. We confine ourselves to presenting the example which Hart gave that is used often in discussions of jurisprudence: the rule that prohibits anyone from bringing any vehicle into a park. His
analysis is:
A law might say, ‘vehicles prohibited in the park’. The concepts o f‘vehicles’ might have had a clear meaning when the law was written, and therefore no doubt applied to transport such cLS the horse and cart or carriage. But new inventions in later times may widen this concept to include such vehicles such as snowmobiles. Similarly, in layman’s language, a perambulator is a ‘vehicle’; however, a court case will not define it as a vehicle appropriate to the purposes of this law. Legal debate continues as to whether open texture should be handled as an appeal to the true intent of the statute or by some alternate means. This example then falls within the category of ‘hard case’, and cannot be determined without reference to some external material.
A case such as this may effectively be a hard case, and like all hard cases presents complications for legal positivists. Positivism can be seen to be a mild form of rule scepticism. Rules function in easy cases, but not in the hard cases. If the positivist view of legal reasoning is correct, then knowledge-based systems can only be built to handle easy cases. If so, then knowledge-based systems have a somewhat limited utility in law since it is hard cases that are litigated and require legal research by the practitioner. As a consequence of these kinds of concerns a new camp of legal theory arose. This new camp attempted to answer a range of concerns about legal positivism and other schools. Its scheme of theory is known as legal realism.
1.3.2 Legal Realism
The two most important facets of the realists’ idea seem to be their rule scepticism and their concentration on the courts’ role is settling disputes. Realism attempts to be both practical and pragmatic. The essence of the approach is that there is more to law than the mere logical deductive application of rules. Its supporters are not saying that there is no Chapter 1. Introduction 9 value in the logical application of legal rules to factual situations. The realists’ attitude prefers rule analysis plus a sociological approach. Thus according to them: ‘Law is as law does’. A science of law should be built upon a study of the law in action. True ideas are those that work in practice. Law is, essentially, ^What legal decision-makers do\ It is not to be discovered just in mere rules. Thus, another name for legal realists is the term ‘rule sceptics’.
Perhaps the most authoritative school of rule sceptics has been the American legal realism movement. Oliver Wendell Holmes is one of the eminent figures in this movement, with his famous statement:
The life of the law has not been logic: it has been experience. [ (Holmes, 1881),
p p .l]
The American legal realists have criticised legal positivism by pleading that it:
1. Fails to consider that no two caaes can ever be identical;
2. Fails to assume that the identification of concepts and situations is non-deductive;
and
3. Fails to recognise that there cannot be antecedent legal rules binding on a judge
(Wasserstrom, 1961).
The legal realists plead that legal decision-makers make decisions for a range of reasons
which cannot be expressed or at least are not clear on the face of the judgment given. Thus,
according to the realists, it is pointless to claim that a legal decision is reached via the existing rules; rather, the decision is more a reflection of the decision-maker’s biases. Once
the decision-maker has reached a decision based on these biases, then he or she is quite likely to find a legal rule on which to justify the decision.
One popular contemporary and more sophisticated view is that of Ronald Dworkin, who argues that law contains things other than rules - in particular, principles and policies: Chapter 1. Introduction 10
The difference between legal principles and legal rules is a logical distinction.
Both sets point to particular decisions about legal obligation in particular cir
cumstances, but they differ in the character of the direction they give. Rules
are applicable in an all-or-nothing fashion. If the facts a rule stipulates are
given, then either the rule is valid, in which case the answer it supplies must
be accepted, or it is not, in which case it contributes nothing to the decision.
[(Dworkin, 1967), p p .25]
He also argues that principles and policies, which (unlike rules) do not function in an all-or-nothing fashion, must be weighed and balanced:
When principles intersect ... one who must resolve the conflict has to take
into account the relative weight of each. This cannot be, of course, an exact
measurement, and the judgment that a particular principle or policy is more
important than another will often be a controversial one. Nevertheless, it is
an integral part of the concept of a principle that it has this dimension, that it
makes sense to ask how important or how weighty it is ... [(Dworkin, 1967),
pp.27]
Dworkin believes that every case has a right answer and it is not always nreached by the application of rules. He also emphasises that in hard cases the decision-maker should weigh and balance the conflicting interests that are at stake. Balancing or weighing interests, however, is an unfortunate and misleading metaphor as it suggests the existence of an objective scale or criteria where in practice none may be found.
As a matter of fact, the development of automated knowledge-based systems in law could not be more delicate. If we suppose that the legal positivists are right, then we have a settled body of rules. Information technology has long had the tools to deduce conclusions based on these rules. If mechanical jurisprudence were the one and only theory to take into account, then we could apply logic-programming methods mechanically to the exercise of making legal decisions.
If we consider the idea of the legal realists, at least the most pure legal realists, then Chapter 1. Introduction 11 we cannot hope to build legal knowledge-based systems. Since each legal decision is made consistent with a series of factors not described in the judgments and perhaps not even accountable to modelling, then we can never hope to design automated knowledge-based system s.
It appears that in jurisprudence the realistic answer lies between the two theoretical extremes. Few would go along with the suggestion introduced by mechanical jurispru dence that all legal systems can be encapsulated in production rules. Deduction alone is inadequate to describe all aspects of legal decision-making. Thus any system based upon deductive reasoning processes will be incomplete for expressing the complexities present in all legal décision-support systems.
Few would also assent to the idea that all decisions are based on totally inconsistent or inaccessible considerations and biases of legal decision-makers. Surely some of the decisions may be ambiguous, complex or illogical. But all of them? This is a doubtful outcome, and one that most people would not accept.
The extreme views are: [1] Nothing in legal knowledge is rule-like (i,e, a rule-sceptic view), and [2] Everything relevant in legal knowledge is rule-like (i,e. a rule-biased view).
But intermediate views regarding this matter seem to be far more common. According to current thoughts, rules are central in law but are not the exclusive medium for representing legal knowledge. Perhaps then the majority of cases should be decided by something near to a positivist approach. But this is a controversial and difficult issue in the automation of legal décision-support systems. This thesis takes a more simple-minded approach to the difficulties in building legal décision-support systems, as we shall see later, but one that is tenable. It pays at least as much attention to AI as to legal theoretical standpoints.
1.4 The Legal Decision-Making Process
After discussing the nature of law in the light of the two important conflicting legal theories, we come to another question of jurisprudence. The present question is ‘How do legal practitioners make decisions?’ Chapter 1. Introduction 12
It is not at all unusual for the law in action to exhibit little likeness to the structure of rules found in published presentation of statutes and previously-decided case reports, but what is the exact nature of the process that gives rise to this difference? It is in the everyday behaviour of legal practitioners that the legal system takes shape and gets things done. The abstract and often short statements of legislatures are given form and purpose in the choices legal practitioners make about the reach and meaning of their idea of the law.
There is a substantial amount of literature [e.g. (Cardozo, 1921), (Wilson, 1982), (Dyer
& Flowers, 1985), (Wahlgren, 1990), (Bing, 1990), (Wahlgren, 1992)] available on the legal decision-making process. The writers of these works have highlighted a significant number of elements governing the legal decision-making process. For example, legal decision
making is to a great extent a domain-dependent task. That is to say, the legal decision making process and its constituents are influenced by the structure of the appropriate substantive law and by the methodological rules that have evolved in its interpretation and use. Thus, there is no unique legal decision-making model available for all areas of law.
Despite this observation, it is commonly found that a practitioner’s legal decision making (reasoning) process establishes a set of facts representing an actual case, and determines the categories within the framework of law into which each of the facts fits.
Different kinds of actions may follow from this categorisation. For example:
• A client may be advised to take a specific course of action to benefit from some legal
rights.
• Some argument may be put forward before a court.
• A client may be advised to negotiate agreements.
It is not yet clear in general how a legal practitioner actually accomplishes the processes of bringing together the relevant set of facts, and then selecting a relevant legal category, though some past studies have been made to investigate these processes. One such study
(O’Neil, 1987) looked at the behaviour of an expert who was a professor at Harvard Chapter 1. Introduction 13
Law School. Several techniques were used to examine his reasoning, including a protocol analysis^ in which the professor was asked to solve previously unseen cases and describe at each step the facts he was considering and the goals to be fulfilled. The researcher recorded that the categorisation of the legal action and fact collection is a ‘relatively fluid process’. This process nevertheless allows the legal professional to find those rules and facts that would help frame the dispute according to a convenient interpretation of the client’s story.
The researcher sums up that an automated ‘intelligent’system for this kind of activity would need to form adjustable models and theories to clarify all known features of the behaviour of the parties, notice discrepancies and gaps in the model for additional clarifi cation, and then find and reason by analogy from similar previously-decided case reports.
In a nutshell, these look like a specifications for the actions of a highly skilled human. But this experiment seems to fail to provide any clear-cut understanding of the finer details of how a legal practitioner reasons.
Nevertheless, this is unlikely to upset AI researchers excessively, because over large numbers of other specialised domains cognitive scientists and psychologists have not been able to clarify everything about how experts reason and solve problems. In fact, no designers of knowledge-based systems demand of themselves that they should replicate human reasoning, with everything or anything that it might be. All that is demanded is that some aspects of human reasoning should be modelled using symbolic computation.
Instead of detailed replication of human reasoning, the aim is to embody in a computer system, with the aid of symbolic computation, the competencies:
[a] to accomplish some defined tasks to the same level of adequacy as an expert;
[b] to accommodate justifications for decisions made.
Different methods prevail by which a knowledge engineer^ can examine the action of an
technique for isolating the procedures used by a human problem-solver on the basis of a record of selected aspects of his or her problem-solving behaviour for given specific problems,
person specialising in knowledge acquisition. Chapter 1. Introduction 14 expert during the achievement of his tasks and get him to generate some kind of description of expert-level activities. It may be impossible to examine directly the problem-solving process that goes on in the head of an expert, but, by examination of expert behaviour when he or she asks questions about the problem and reaches conclusions, it is possible to construct a model of the reasoning process that will display the desired behaviour
(Wielinga & Schreiber, 1989).
Moreover, formulation and application of law is a flexible partnership between legis lators and decision-makers (e.g. judges, magistrates, registrars etc.). In some cases, the legislators play the dominant role in the partnership by specifying most or all of the detail relevant to the application of the law. In other cases, the legislators specify a limited amount of detail and delegate the responsibility for the remaining detail to the decision makers. These allow for a significant amount of judicial discretionary behaviour on the part of legal decision-makers in the process at each decision point.
Judicial discretion is, for the most part, a mystery - to the general public, to the com munity of lawyers, to teachers of law, and to judges themselves (Miller, 1978). American
Judge Harry Edwards referred to this phenomenon in the following terms:
One might expect that today, more than a half-century after the Legal Realist
movement, the phenomenon of the exercise of ‘judicial discretion’ would have
been so exhaustively studied as to merit no more than a passing reference in
preparation for the examination of more controversial matters. That turns
out not to be true. Not only does the activity of judicial lawmaking remain
mysterious, but a surprisingly large number of people, both within and without
the legal community, question its legitimacy in any form. [(Edwards, 1984),
p p .388]
While discretion has been the subject of thorough discussion, there is really an in significant amount of research combining the AI and law points of view concerning how discretion is actually exercised by legal decision-makers. The study of the nature of ju dicial discretion, in this perspective, must begin with its definition. This is by no means a simple task, for the term discretion has more than one meaning, and indeed means Chapter 1. Introduction 15 different things in different contexts. Galligan illustrates the difficulty by saying:
[T]he extent to which officials, whether they be judicial or administrative, make
decisions in the absence of previously fixed, relatively clear, and binding legal
standards. Frequently it appears that decisions are made and so power exer
cised according to considerations which vary from one area of state activity to
another, from one type of institution to another, and even from one set of cir
cumstances to another, in the absence of that pattern of normative standards
and principles which generally is thought to be so central to the very notion of
legal order. [(Galligan, 1986), pp.l]
Therefore, judicial discretion means the power the law gives the decision-maker to choose among several alternatives, each of them being lawful. This definition assumes, of course, that the decision-maker will not act mechanically, but will weigh, reflect, gain impressions, test, and study. However, this self-conscious use of the power of thought does not say what the result of judicial discretion will be. It only prescribes how the decision-maker must act within the framework of his or her discretion. Moreover, judicial discretion, by definition, is neither an emotional nor a mental state. It is, rather, a legal situation in which the decision-maker has the liberty to choose among a number of options.
Thus, discretion assumes a zone of possibilities rather than just one point. The zone of lawful options may be narrow, as when the decision-maker is free to choose between only two lawful alternatives. Or the range of lawful options may be considerable, as when the decision-maker faces many lawful alternatives and combinations of alternatives. In this sense, legal sources distinguish between narrow and broad discretion.
Further, as we know, legal decision-makers cannot make law out of air and they have to abide by legal norms. Thus the natural question arises: What are the sources of judicial
discretion! The main sources of judicial discretion are; statutory norms, principles and
case law (i.e. previously decided case decisions).
Every statutory norm has a linguistic element which is expressed in words. A statute may have a complex structure with numerous intertwined conditional clauses and cross- references. Therefore, a legal decision-maker has considerable freedom of reasoning within Chapter 1. Introduction 16 that part of the legislation. Given a particular case, the legal decision-maker interprets that case in the light of the statutes in the particular area and uses the freedom in constructing justifications for possible decisions specific to that case. In case law also, previously- decided case decisions are the evidence of the law and a particular outcome of adjudication.
The reasons for the existence of judicial discretion in case law are for the most part, and in their general sense, the same reasons that produce discretion in statutory law - namely, the lack of clarity in the terms. The problem of precedents (i.e. previously-decided cases) is not just in the words themselves, but also the concepts that the decision-maker wants the words to communicate to the reader. These concepts have clear implications in one type of case, yet they may lead to doubts about their implication in another type of case. Indeed, case law, like statutory law, reflects principles, policies, and standards. The uncertainty that exists in the statutory law also exists in case law, and with greater force, precisely because there is no binding text to guide the delineation of the borders of the concepts.
Principles are rules of behaviour that are based on ethical values such as fairness, justice, and morals. Principles serve various functions in the law; the determination as to the situations to which the principles will apply is in the hands of the decision-maker.
We can say that in some sense discretion is a manifestation of ambiguity or context- dependence of words. A human expert can determine whether a word has a number of meanings. Therefore it is reasonable to argue that the knowledge engineer should take account of these varying meanings in the knowledge base design. This can be done by framing alternative rules from which the user can choose, depending on the user’s opinion regarding the context of the situation.
This is not to say that modelling legal decision-making is so easy, nor indeed to argue that legal knowledge-based systems are applicable in all cases. At the same time, judicial discretion is an issue lurking behind the design of any legal décision-support system. Even if one ignores it, one is in effect making a choice about how the resulting systems handle discretion.
It is often both difficult and undesirable to make a legal rule precise to the extent that there is only one way that a legal practitioner can reasonably apply it. Few rules Chapter 1. Introduction 17 are so clear that they can be used like an on-ofF switch. The person using the rule cannot always say with certainty ^yes, it applies’or Ht does not apply here’. This means that legal practitioners must often resort to their own judgment in deciding whether a rule applies or not. In addition, legal decision-makers are as susceptible as anyone else to new or changing social attitudes toward the authority of rules. Because of these considerations, nobody has given any convincing simple straightforward method of determining under arbitrary conditions whether or not a rule should apply.
In thinking about how the law can best serve its purposes, legal décision-support systems designers frequently run into what may be described roughly as a conflict between pressure to treat rules as in a simple expert system, where there is no context in which the rules are interpreted (i.e. the rules themselves are the context), and pressure to give someone discretion in interpretation. In designing a typical legal décision-support system one has to give certain amount of discretionary power to its user. The way in which we do this in the system ASHSD may look a little artificial or restricted by comparison with the free-ranging discussion above, but we try to comply with as much of the spirit of the discussion as is allowed by the knowledge-representation formats (rules and cases) and their standard tools of reasoning that are available from current computer science. In our rule-base, for example, the rules work in the usual sense of computer science or logic.
‘Discretion’ is something for the user to consider at just two stages. Firstly, when ASHSD enquires for relevant facts, the user decides what is relevant or true, and presents this as input. Secondly, after ASHSD produces output from its rule-based section, the user is free to decide if a suggested conclusion is acceptable or if some external consideration requires it to be set aside. The same interpretation applies to ASHSD’s case-based side.
The system selects the most similar cases with respect to the new case by a standard AI approach, and then it is up to the user to interpret the decision parts of those cases in the light of the present case.
We do not believe that judicial discretionary power can be automated. The main reasons are: [1] It is difficult to replicate totally a multitextured process such as legal decision-making, and [2] Legal decision-making is humanistic rather than mechanistic.
Therefore, mechanised discretionary power seems possible only within restricted limits.
This implies that the automated systems can help the human decision-maker by offering Chapter 1. Introduction 18 appropriate information at the time of need and then leaving it to the user to decide how to process the supplied material.
1.5 Model for the Present Research
Most contemporary researchers of AI and law are of the opinion that it is jurispruden- tially acceptable to build a legal knowledge-based system using a rule-based knowledge representation plus logical deduction for ‘easy’ cases. Even it is possible to build legal knowledge-based systems for ‘easy’ cases, we are still left with the ‘hard’ ones. The issue of what to do with the latter seems insurmountable for the rule-based paradigm. How ever, as a case-based paradigm is available with AI, this issue may actually not be so much of a barrier. One possibility is to build a system which, given the facts of a case, will retrieve the relevant cases (for and against a certain decision) for human inspection.
The human user may then be able to decide whether the case at hand is easy or hard and use the output of the system to draw conclusions. This level of computer-based help is now achievable. Credit goes to the development of case-based reasoning (CBR)^ [e.g.
(Riesbeck &; Schank, 1989), (Kolodner, 1989)] where most of the research responsible for it has been carried out since the first rule-based legal expert systems received their initial publicity. It is claimed that one of the advantages of GBR is:
[M]any real world domains are so complex that it is either impractical or im
possible to specify all the rules involved; a case, however, can still provide a
‘solution’; CBR does not require a strong model or deep understanding of the
dom ain ... Domains without strong models are domains in which there is no
theory from which answers to problems can be deduced, or domains where these
theories are partial. [(Hahn, 1993), pp.234]
^Case-based reasoning is an approach to problem-solving in Al and cognitive science that uses a stock of specific past experiences (e.g. previous legal cases and their decisions). Chapter 1. Introduction 19
The British legal system and the legal systems of many of its former colonies are based upon th e ‘common law’ system. Most of the rules and principles of the common law have been developed and further elaborated in the daily work of the courts in resolving the disputes brought to them. In this process, the common law has a tradition of using both statutory law and precedents. But in the major continental jurisdictions (also including
Scotland), where the foundation is the ‘civil law’ system of the Romans, statute and codified law have played the leading part in their legal systems. A large reliance has been placed on precedents in the common-law domain, while precedent is not a dominant issue in the civil-law countries.
Given the general historical background, it is now therefore appreciated that good common-law knowledge-based systems are likely to call for two forms of knowledge that can encapsulate both statutes and precedents. From the viewpoint of the present re search, we have set out and explored a way of combining the CBR character of common- law decision-making and the positivist aspect of the legal decision-making process (i.e. rule-guided reasoning). One may argue that legal decision-makers often do not express every feature of their decision-making process in a case report. We would like to say that suitably relevant previously-decided cases can underpin at least some of the reasoning process that one cannot encapsulate in rule-based reasoning. Therefore, in our research, we have adopted a simple model (probably the most structurally simple, but no previous researchers have reported, either positively or negatively, on such an approach) that com bines these two reasoning methods (rule-based and case-based) in a linking framework. It should be remembered that our décision-support system is just to support certain kinds of people interested in using legal knowledge, and not to replace them.
Past legal researchers [e.g. (Susskind, 1987a), (Gardner, 1987), (Wahlgren, 1992),
(Zeleznikow & Hunter, 1994)] have written comprehensive accounts about the design pro cess for legal knowledge-based systems. In these and elsewhere, there is a clear consensus about the need for examination of legal knowledge-based system design on three fronts:
[1] Identification and acquisition of knowledge
[2] Representation of the collected knowledge Chapter 1. Introduction 20
[3] Reasoning on the represented knowledge
1.5.1 Identification and Acquisition of Knowledge
Certain steps in the building of any knowledge-based system, between design and the provi sion of the first adequate working system, are known as knowledge engineering. The process of knowledge engineering has been rather well analysed in AI literature [e.g. (Hayes-Roth et al., 1983), (Coenen & Bench-Capon, 1993), (Schreiber et al., 1993)]. The term knowl edge engineering is used for the process of transferring and transforming knowledge about expert problem solving from the knowledge sources to a computer program. Knowledge engineering can be decomposed into different phases. The details of these phases can be found in any standard textbook in this subject.
In the initial stage the substantial material that is going to be used in the proposed system implementation must be identified. Once the identification is over, the knowledge acquisition phase proper starts. As regards the elements of substance, in many situations
(particularly when no expert information is regularly available) it is important to analyse all the accessible written material on a given issue. In this phase also, any people who are going to participate in some way as experts must be approached. In relation to this, and in order to be able to choose proper methods for future examination, one should also try to estimate whether or not and to what extent the required knowledge is of an implicit and hidden nature.
Widely accepted sources of legal knowledge are statutes and precedents. In an ideal world there should be nothing to prevent the whole of the law being set out clearly and logically in statutory form. However, since the statutes are written in natural language they are subject to interpretation. Therefore, it is not surprising that even the simplest rules in or obtainable from the statutes may be open to multiple interpretations.
Apart from statutes, the next most significant influence on the common law is prece dent. Thus when a legal decision-maker interprets a new case, similar cases in the past are usually referred to for guidance. This is known as the doctrine of precedent. The doctrine of precedent covers two types: binding precedents, which judges are bound to follow, and Chapter 1. Introduction 21 non-binding precedents or those that are merely persuasive. A further dimension in deal ing with the doctrine of precedent involves the rank ® of the court in which the particular precedent was decided. The general attitude is that a higher court’s decision binds lower courts and that some courts bind even themselves. This is known as the hierarchy of authority, as treated in much more detail by Glanville Williams (Williams, 1978).
A way needs to be found between accounts of the doctrine o f precedent a,nd descriptions of th e practice of handling precedent. The doctrine consists of the rules that prescribe how previously-decided cases must, may and can be used; descriptions of practice deal with the techniques that are in fact used by legal decision-makers in handling the precedents.
The distinction between doctrine and practice is not a sharp one. On the one hand, there are tacit conventions regularly followed by legal decision-makers, which give greater respect to previously-decided cases than orthodox formulations of the doctrine may seem to demand. But the doctrine of precedent does not lay down a formula for extracting rules of law from previously-decided cases, that is, for determining the ratio decidendi.
Nevertheless there is a tacit convention that special attention should be paid to the words used by legal decision-makers in previously-decided cases, and often a literal passage from a judgment in some previously-decided case is treated as an adequate formulation of th e ratio. Those features of a case that are said to be important criteria make up the ratio decidendi. It is not uniformly clear how this is to be handled, because a given case may be similar to many precedents with inconsistent decisions. Thus, any human legal decision-maker is likely to depend on a substantial amount of analysis; even deciding about which precedents are similar to the present one is open to personal interpretation and can lead to prominent differences in judgment. It is not a straightforward process but an art that one learns slowly through exercise and study.
®Besides distinguishing between civil and criminal courts, there are various ways of categorising English courts. There are inferior courts and superior courts. An inferior court is any court that is not the High
Court (and, of course, the appeal courts), the Central Criminal Court or Crown court. More details are given in many introductory textbooks on law.
®The ratio decidendi can be read as ‘reason for decision’, i.e. the legal reasons why the judge came to the conclusion that he or she did. Chapter 1. Introduction 22
However, one can present a suitable example of the method involved. W hat the doc trine of precedent says is that cases must be decided in a similar fashion when their material facts are the same. Nevertheless it does not mean that all the facts should be the same. In real life the facts of a case will rarely repeat exactly for another case; but the legally material facts may recur, and it is with these that the doctrine is concerned.
One can give a simple example to explain the doctrine of precedent as below;
Let us assume that a particular decided case (say CASEOl) is made up of facts FACTO 1,
FACT02 and FACT03. We also assume that the legal decision-maker for this case reaches a conclusion CONOl based on the view that the facts FACT02 and FACT03 are m aterial and FACTO 1 immaterial. Then according to doctrine of precedent we can say that in any new case in which only the material facts FACT02 and FACT03 exist, the conclusion must be CONOl. If in a new case facts FACTO 1, FACT02, FACT03 and FACT04 exist, and fact FACT04 is held to be material, the first case will not be a direct authority. However, it may be a similar case for the purpose of helping to draw conclusions.
Thus the most general question is now - what facts are legally material? The answer to the question is that the material facts are the most important facts of a case, on which the final decision is based. For example, in a matrimonial-home-settlement case Hhe husband left matrimonial home after having a domestic problem’is a material fact. But whether the husband’s name is Tom, Dick or Harry does not make any difference for the final decision, and this is why the name of husband is not a material fact. This is a very obvious kind of example, but sometimes it requires much practical experience to find out the material facts of a case.
The jurisprudential literature on the subject of whether and how to determine the ratio decidendi is very extensive, and it appears to be without a general consensus. Nevertheless,
Susskind brings evidence to support the view that a determination is practically always possible. On this subject, he quotes:
Any honest description of the use of precedent in English law must allow for
the following pairs of contrasting facts. First, there is no single method of
determining the rule for which a given authoritative precedent is an authority. Chapter 1. Introduction 23
Notwithstanding this, in the vast majority of decided cases there is very little
doubt. The head-note is usually correct enough. Secondly, there is no author
itative or uniquely correct formulation of any rule to be extracted from cases.
On the other hand, there is often very general agreement, when the bearing of
a precedent on a later case is in issue, that a given formulation is adequate
[(Hart, 1961), pp. 131].
Any view like the above raises a central question for jurisprudence and therefore also for legal systems: If we are able to find statutory and/or case law (i.e. previously-decided case decisions) that covers or acts as precedents for any current legal case, does this mean that the entire common-law system may be reduced to a collection of such knowledge-sources?
If the answer is affirmative, how can we represent these knowledge-sources? If there is more to law than these items, what is it? and how do we confront it in a computer system? There are major issues in jurisprudence which are beyond the scope of this thesis in computer science. But for the present research, we have had to confine ourselves primarily to three legal knowledge sources: statutes, legal text-books, and previously-decided case reports. We have used these sources for our knowledge acquisition process, whose details are described in chapter 3.
1.5.2 Representation of the Collected Knowledge
Once knowledge is collected from legal sources, one should find effective ways to represent it in forms suitable for computation. This must be done in a way that permits inferences to be drawn. Thus the process of knowledge representation focnses also on the type of the inferencing techniques to be used, as well as the richness and diversity of knowledge to be represented.
Quite a large variety of knowledge representation techniques is available. But the nature of legal knowledge, as we have described it above, leads as naturally to just two: rule-based and case-based.
T he term rule is sometimes reserved for one type of knowledge representation which Chapter 1. Introduction 24 is the simplest form alism expressing knowledge so as to enable computers to perform to a certain extent as an expert consultant. At the heart of the rule-based systems are the formalisms for describing task areas, as well as the rules of behaviour or of the decision making process, such as cause/effect, situation/action etc. These two-part structures are instances of:
I F
Symbols and operators combine into structures which include concepts, knowledge, and facts. Such structures may be simple or complex, and emphasis is on symbolic repre sentation and manipulation.
In order to make use of any previously-decided cases by referring to case-report infor mation, it is necessary to represent the case reports in some suitable data structure that can be manipulated by programs. Each of the two methods of legal knowledge represen tation mentioned here therefore offers a natural support for representing some of the legal knowledge for the project described in this thesis. We give further details in chapter 4 and 5.
1.5.3 Reasoning on the Represented Knowledge
To utilise the represented knowledge, knowledge-based computing systems should have general problem-solving methods providing reasoning schemes for all the preferred repre sentations. The following quotation from Blackburn’s Dictionary of Philosophy (Black burn, 1994) is a fairly typical example of an elementary introduction to reasoning in general:
Any process of drawing a conclusion from a set of premises may he called a
process of reasoning. If the conclusion concerns what to do, the process is
called practical reasoning, otherwise pure or theoretical reasoning. Evidently
such processes may he good or had: if they are good, the premises support or Chapter 1. Introduction 25
even entail the conclusion drawn; if they are bad, the premises offer no sup
port to the conclusion. Formal logic studies the cases in which conclusions
are validly drawn from premises. But little human reasoning is overtly of the
forms logicians identify. Partly, we are concerned to draw conclusions that ‘go
beyond’ our premises, in the way that conclusions of logically valid arguments
do not. Partly, it has to be remembered that reasoning is a dynamic process,
and that what to a logician looks like a static contradiction may be the sensi
ble replacement of one set of assumptions with others as the process develops.
Furthermore, as we reason we make use of an indefinite lore or common-sense
set of presumptions about what is likely or not. A task of an automated rea
soning project is to mimic this casual use of knowledge of the way of the world
in computer programs.
It can scarcely be a controversial view that there are at least three types of reasoning to be found in the contexts of arguments on points of law: rule-based reasoning, case-based reasoning and hybrid reasoning (a combination of reasoning methods).
1.5.4 Notion of Rule-Based Reasoning
Generally speaking, we live in a rule-dominated world. In ordinary talk the word rule has many usages. W.L.Twining has written a considerable amount (Twining & Miers, 1991) on this point. Rule is used to mean a general norm^^mandating or guiding interpretation, conduct or action in a given type of situation. The implication of a rule is based on fact(s) and those facts must be hold good for that particular rule to be applicable.
Reasons and rules are interconnected. Most facts that are reasons have an underlying rule that makes them into reasons. The notion of a legal rule in a very broad sense includes
(among other things) principles of law, criteria for the use of words, and rules of evidence.
Rules in this sense connect facts of one type to facts of another type. The former are the reasons, the latter are what they are reasons for. Examples of rules are:
’a norm can also be a conditional statement, i.e. one expressed in the form ifp then q. Chapter 1. Introduction 26
[a] A fruit that is filled with a milky fluid, is approximately oval in shape and has a
thin hard shell enclosing edible white meat, is a coconut {rule of classification).
[b] Honesty should be rewarded [deontic rule).
[c] Somebody who comes running out of a house with a gun under suspicious circum
stances may be taken to be a criminal {rule of inference).
[d] Homosexual individuals are not allowed to join the army {alethically modal rule).
Rules can be divided into a condition-part and a conclusion-part. The condition- part indicates which facts count as a reason; the conclusion-part tells what they are reasons for.
The nature of law has been analysed extensively by legal philosophers. Their con clusions range from law as being simply a set of rules to law as reflecting no more than conscious and subconconscious attitudes, beliefs, and procedures which are peculiar to the parties, the witnesses, and the facts of each case. Even if it is conceded that law can be analysed satisfactorily as a set of rules, it can still be contended that legal rules do not possess the rigidity of, for example, scientific rules. The application of scientific rules tells us things like :
The volume (V) of a fixed mass of gass, at a constant temperature (T), is inversely proportional to its pressure (P). This is known as Boyle’s law and it can be expressed symbolically as:
V (X ^ {when T is constant)
However, it is doubtful whether the same can be said of legal rules. In Phillips v
B ro o k s:^^
Thi Hips V Brooks, Limited [1919] 2 K.B. 243 Chapter 1. Introduction 27
The plaintiff, who was a jeweller, sued the defendants, who were pawnbrokers,
for the return of a ring or, alternatively, its value, and damages for its deten
tion.
On April 15, 1918, a man entered the plaintiff’s shop and asked to see some
pearls and some rings. He selected pearls at the price of 25501 and a ring at
the price of 4501. He produced a cheque hook and wrote out a cheque for 30001.
In signing it, he said: ‘You see who I am, I am Sir George Bullough, ’ and he
gave an address in St. James’s Square. The plaintiff knew that there was such
a person as Sir George Bullough, and finding on reference to a directory that
Sir George lived at the address mentioned, he said, ‘Would you like to take
the articles with you V to which the man replied: ‘You had better have the
cheque cleared first, but I should like to take the ring as it is my wife’s birthday
tomorrow, ’ whereupon the plaintiff let him have the ring. The cheque was
dishonoured, the person who gave it being in fact a fraudulent person named
North who was subsequently convicted of obtaining the ring by false pretences.
In the meantime, namely on April 16, 1918, North, in the name of Firth, had
pledged the ring with the defendants who bona fide and without notice, advanced
3501, upon it.
The plaintiff said that when he handed over the ring he thought he was con
tracting with Sir George Bullough, and that if he had known who the man
really was he would not have let him have it. In re-examination he said that he
had no intention of making a contract with any other person than Sir George
Bullough.
The court held that looking up of the address in the directory in face-to-face transac tions was not sufficient to establish an identity, so as to make the contract void. Applying
Phillips V B rooks the court should have concluded, if legal rules had rigidity, that there was no mistake as to identity. However, the court came to the conclusion that there was a mistake as to identity, thereby making the contract void.
Unlike scientific rules, legal rules can never be measured against the benchmark, be cause our theories of the law are often nebulous and there is usually no one correct answer. Chapter 1. Introduction 28
Unlike scientific domains, particularly those that can be modelled using rule-based sys tems, law has a subjective element. Each case is capable of raising difficult issues, such as society’s current values, individual responsibilities and the contemporary viewpoint as to what is justice. Therefore the rules of the scientific world and legal rules do not function in the same way. Any application based on legal rules requires translation of abstract law into solution for particular cases. The main emphasis is on interpretation instead of abstraction (MacCormick & Summers, 1991). There are various and quite different approaches taken by researchers to build prototype systems that reason with legal rules.
The rules that are encoded in an automated system may be sections from a statute, the formation of rules out of previously-decided case reports, or some other readily explicable series of rules. An automated system generally stores the body of rules in a structure that is known as a rule base.
Knowledge-based systems relying on rules have proven quite helpful in constructing le gal décision-support systems. Such systems [e.g. (Johnson & Mead, 1991), (Bench-Capon
& Coenen, 1991b), (Sergot, 1986)] have often been used to determine whether a person satisfies certain conditions; for example, whether somebody is eligible for unemployment or social security benefits. In consequence, these early knowledge-based systems amounted to little more than collections of rules relying on static necessary and sufficient conditions and meta-rules chained together. However these rule-based systems are limited to domains w ith clear legal rules or, in Richard Susskind’s phrase ‘easy cases’. The suitability of these rule-based reasoning systems for open-textured problems is often questioned.
Another significant limitation of present legal knowledge-based systems is their failure to address explicitly the issue of legal discretion. We have discussed this already at some length, appreciating its difficulty, and towards the end of section 1.4 we state a position
(reflected in our choice of problem domain, which allows the position to be tenable) that shares the responsibility for dealing with it between the system and the user.
Apart from rule-based reasoning (RBR), current AI research in the legal domain has focused on how to deal with previously-decided cases, and how to develop legal knowledge- based systems that use previously-decided cases to perform case-based reasoning. Chapter 1. Introduction 29
1.5.5 Notion of Case-Based Reasoning
Case-based reasoning (CBR) seeks to overcome many of the limitations of a rule-based approach. CBR is common and extremely important in human cognition. It has emerged in the mid-1980s as a significant reasoning method. This type of reasoning is based on the observation that human reasoning processes are founded on specific experience rather than a set of general guidelines. Thus compared to RBR, CBR is a process of considering past cases and arriving at decisions on comparison between the current situation and the old cases.
A legal CBR system will use previously-decided cases (potentially precedents for future decisions), comparing the relevant facts of a new case with that of the material stored in the form of cases. The aim of a CBR system in legal reasoning is to find the cases that are most similar to the new case and that may therefore act as precedents for how to decide the new case. Once the most similar cases have been determined, an advanced
CBR system could then use these potential precedents in two ways. Firstly, it could engage in constructing arguments by interpreting the new case in the context of some suitable retrieved material. Secondly, it could suggest a possible solution to the new case by some process of interpretation of the candidate precedents. (In our work, we make no claim about constructing legal arguments, and Hnterpretation’involves simply a taxonomic estimate of similarity, as in chapter 6).
Reasoning from precedents in order to interpret a new case (much as legal practitioners do) or creating a equitable solution to a new problem (much as legal mediators do) is legal
CBR. This activity raises a variety of research issues, which researchers [e.g. (Slade,
1991)] are addressing. Some of these, e.g. on the nature of interpretation or the details of multiple steps of reasoning which (so to speak) conduct arguments with each other, are not yet treated adequately in any work dealing with computation, and they are certainly beyond the scope of this thesis. Present automated legal CBR systems, which can be said to arrive at useful conclusions by means that do not strongly reflect the behaviour of legal specialists, have nevertheless proven quite helpful in the study of legal decision support
[e.g. (Bain, 1986), (Ashley, 1987; Rissland & Skalak, 1989c), (Smith & Deed man, 1987)].
Despite their successes, the legal knowledge-based systems relying only on CBR ideas Chapter 1. Introduction 30 have their limitations also. The main limitation of these systems is that they use only the precedents without considering other legal knowledge sources (e.g. statutes, codes, etc.).
The limitations of stand-alone RBR or CBR systems are now pushing researchers towards considering other reasoning methods to automate legal décision-support systems.
Since legal practitioners make use of at least statutes and precedents, legal knowledge- based systems ought to do likewise. Present researchers [e.g. (Rissland & Skalak, 1989b),
(Vossos et al., 1991), (Pal & Campbell, 1996a)] are giving more emphasis to the issue of how to integrate reasoning on both legal rules and previously-decided cases in legal knowledge-based systems.
1.6 Subject Area of the Research
“More than one in three marriages will end in divorce; countless children suffer because of it. In terms of human misery divorce can be both cause and cure .. ”
[The Sunday Times Magazine; October 18, 1992]
The above quotation reinforces the point that divorce is a common problem in our society. The ancillary (to the divorce) disputes about property are much more common than disputes about the divorce itself and usually involve protracted negotiations between the solicitors representing the parties involved, sometimes only being finally resolved before a registrar or judge. Whilst there are criteria to be considered by the courts when making any order concerning a property dispute, there are few definite rules, so that courts are in effect compelled to apply their own interpretations of the general criteria to the specific case before them.
Trends indicate an ever-increasing burden on the decision-making powers of legal prac titioners, who have already shown signs of strain. In addition, information about recently- decided previous cases is threatening to bury the legal practitioners under reports. Legal practitioners must have the tools to enable them to analyse and organise the vast quanti- Chapter 1. Introduction 31 ties of information available if they are to use it effectively for decision-making purposes.
Its potential in legal research is only just beginning to be realised.
1.6.1 Project Orientation
A goal of this project is to develop a computer-based system to suggest decisions on new cases, by exploiting both general legal rules and specific case-based information. T he primary goal, however, is one in computer science: to demonstrate a new and simple but effective scheme for hybrid reasoning. We have mentioned already that the British legal system relies heavily on a mixture of statutes and previously-reported cases. Its strength lies in the combination of rules and case-based information because cases appear to be particularly well suited to handling exceptions to the rules. This research project provides an example of just such a use of both cases and rules in one system to achieve better performance. Therefore, the role of CBR in the system architecture is to improve on the effectiveness of a purely RBR approach, and we can also show evidence that vice versa is true.
The legal practitioner’s task is to interpret the abstract law into decisions for par ticular cases. The idea is to amplify the legal rules rather than simplify for particular cases. Therefore, one needs to go beyond the statute itself to other sources of knowledge, particularly cases and even to historical background about statutes. This demand forces one to reason with previously-decided similar cases (i.e. with precedents) relevant to the new case.
The advantage of CBR in this context is that it uses the decisions of previously-decided cases to analyse or solve a new case. Therefore, previously-decided cases can exert an effect on the decision-making process. They provide a reasoning framework for interpreting the present case, helping the decision-maker to identify specific aspects of the situation that were important in the past. The decision-maker is not guaranteed to make the best or right decision by referring to past cases. Thus, on its own, CBR is not comprehensive for every case and an approach is needed to encompass the relevant principles from both paradigms (i.e. RBR and CBR) and apply these with additional interpretation to find a Chapter 1. Introduction 32 more appropriate solution. The model described here is implemented in a computational hybrid framework, namely Advisory Support for Home Settlement in Divorces (ASHSD),
1.6.2 Why a Hybrid System?
Many researchers [e.g. (Ashley, 1990), (Branting, 1991), (Rissland, 1990)] have advocated case-based reasoning in the legal domain and a few of them have attempted to design hybrid systems, like CABARET [(Rissland & Skalak, 1989a), (Rissland h Skalak, 1989b)],
GREBE (Branting, 1991) and IKBALS II (Vossos et al., 1991). In many legal situations, it is difficult for the decision-maker to reach final decisions using legal rules of thumb. A rule may have unspoken prerequisites or may depend on a totally new situation. The role of CBR in the system architecture is to improve on the effectiveness of a purely rule-based approach. Notably, the area of ‘matrimonial home settlement in divorce (family law)’ is clearly appropriate for the application of such a hybrid system.
For instance, the M esher Order^'^ is well known in matrimonial home settlement after divorce. It is rule-like, though to call it a rule is something of a misnomer - it has never been more than a general rule of thumb. Initially the English courts adopted the M esher
Order approach as a guide to preserve the home for the child of the family during minority.
However, this form of compromise between retention and sale of the family home will only be appropriate under certain circumstances, and the boundary between appropriate and inappropriate conditions is still being refined after 20 years of case-based experience.
The courts have given much attention to satisfying the need of the parties for a secure home. Time and againthe Mesher Order has been out of favour with the courts in
Mesher v Mesher and Hall [1980] 1 All ER 126n, CA (originally 1973). This form of order, which derives its name from the case in which it was made, requires that the home be held on trust for sale in equal shares, and the sale postponed until the dependent children of the family reach a certain age (say,
17 years), during which time the custodial parent would be able to live in the matrimonial home.
^^See, for example, the following cases where the Mesher Order has been set aside: Martin v Mcirtin
[1978] Earn 12; Chadwick v Chadwick [1985] FLR 606; Clutton v Clutton [1991] 1 FLR 242, Knibb v Knibb [1987] 2 FLR 396. Chapter 1. Introduction 33
COMPREHENSIVE ADVICE AND JUSTIFICATION
PARTIAL ADVICE AND JUSTIFICATION
NO A D VICE
RULK-llASKI) KKASONINC RULE-BASED ADVICE c>
CASK-BASED REASONING SELECTION OF SIMILAR CASFX AND
DISPLAY OF THE CASE DETAILS
WITH FINAL JUDGFIMENT SUITABILITY OK REASONING METHOD(S)
CASE-BASED ADVICE
M EN U O I-nO N S
INDICATION OF SUITABILHT OF
r f ;a s o n i n g m e t h o d (S)
SUITABILITY OF REASONING METHOIHS)
Figure 1.1: The computational framework
individual cases. Nevertheless, it is still currently in widespread use.
1.6.3 Overview of ASHSD
ASHSD presents solutions to the research problems through the use of a nested case representation approach to about 50 English divorce cases and 190 rules for matrimonial- home-related problems. The system considers three aspects of matrimonial home settle ment, namely owned home settlement, transfer of tenancy, and the severity of injunction.
.A.SHSD has been developed as a pure research product. The software is implemented in a portable dialect of LISP^"^. ASHSD uses two types of reasoning methods: rule-based reasoning (RBR) and case-based reasoning(CBR). It includes a text-based user interface and collects facts for a new case in a question-answering session with its user. The user can select either reasoning method (i.e. RBR or CBR), or indicate no preference. In the process of consultation with ASHSD, the user can examine either or both rule-based and case-based information to formulate a suitable solution for the new case in hand. The
'“'XLISP is an item of free software for academic use on a variety of hardware platforms. It is available by anonymous ftp [ File Transfer Protocol] from the Internet address 128.95.10.115. Chapter 1. Introduction 34 computational framework of ASHSD is shown in Figure 1.1.
The rule-base component consists of two types of rule: available-action(s) rules, and prediction rules. The available-action(s) rules are explicit in the legal sources (statutes, case reports, etc.), and determine whether or not a court has the power to act or take a specific action. For example, in a severity of injunction case the court has a range of options available as below:
• non-molestation order
• non-molestation order attached with a power of arrest
• exclusion order
• exclusion order in conjunction with a power of arrest
The available-action(s) rules determine which of these options are applicable for a case.
The prediction rules explain how the courts are likely to act within the range of options available, which is circumscribed by the available-action(s) rules. Thus, the prediction rule illustrates how the court is likely (on the basis of relevant past episodes) to exercise its discretionary power for particular cases. We can say, for the purpose of distinction, that the available-action(s) rules give available-action(s) on the available options and the prediction rules provide prediction about what a court may conclude for a particular situation. Both types are contained within the rule-based advice part of ASHSD. Although it is not unusual in the literature on expert systems for legal applications to find papers that do not distinguish between these two types of rule, the rest of the thesis distinguishes between them. Unless it is specifically mentioned, it will be assumed in later chapters that the rule-baaed analysis is founded on the prediction rules.
In our rule-based analysis, the available-action(s) rules are used in one of two modes: response, or no response. A response is produced when all the preconditions of an available- action(s) rule are matched by the facts of a new case, as in a conventional expert system.
However, when this situation does not hold, such rules are not considered further. In Chapter 1. Introduction 35 particular, no attem pt is made to select items of possible interest by computing a weighted score of those preconditions that happen to be true. The justification is that the available- action(s) rules either apply to a case on the basis of certain special facts about it or do not, and that questions of how a court might proceed if that information were interpreted somewhat differently do not arise.
The prediction rules can generate any of the three types of output: clear-prediction^ speculation, and no-prediction. Clear-prediction is possible when at least one of the pre diction rules has triggered as a consequence of the facts of the new case. The system presents what we can call speculations by applying a weighting criterion to the rule pre conditions that are true, even when none of the rules is triggered. A speculation consists of conclusions that would have followed if all the preconditions of a rule that has some relevance has been true, plus output focusing on the failed preconditions (i.e. reasons why a conclusion cannot be accepted without reservations).
The first step in generating a speculation is to identify the rules that are nearly trig gered. A scoring mechanism is used to determine which rules are closest to triggering.
We have found by experiment that there is a consistent threshold in our score, below which any information that ASHSD may give is unhelpful. The system, therefore, offers no information unless at least one of its prediction rules has a score above the threshold.
If there is no such score, we say that the output is of a no-prediction type.
When a user ask for rule-based advice, ASHSD can provide one of three possible op tions: comprehensive advice, partial advice, and no advice. In comprehensive advice, the system provides the possible available-action(s) plus a prediction of a court’s decision, provided that at least one of the prediction rules has triggered. The partial rule-baaed ad vice can be in one of two categories. For category-one of partial rule-based advice, ASHSD offers the relevant available-action (s) and also presents a speculation (in the sense in which we have defined that term). The category-two of partial rule-based advice produces no prediction or speculation but does suggest some valid available-action (s). Finally, the sys tem provides no rule-based advice at all when it fails to come up with available-action (s) or any kind of predictive information. These different types of rule-based advice are shown diagrammatically in Figure 1.2. Chapter 1. Introduction 36
RES RES RES NRE
CPR SPE NPR NPR
Com prehensive Partial Advice No Advice A d v i c e
: LEGEND:
RES = Response NRE = No-response CPR = Clear-prediction SPE = Speculation NPR = No-prediction
Figure 1.2: Different types of behaviour leading to rule-based output
When the case-based side of ASHSD is invoked, the cases that have the highest sim ilarity rating with respect to a current problem are retrieved, and presented according to how closely they match the problem. To determine the measure of similarity, ASHSD uses the ideas and general approach of numerical taxonomy^^ (Sneath & Sokal, 1976). If a measure of similarity taken over the cases in the case base is always below a threshold, which we have validated by extensive trials, ASHSD makes no recommendations. The relevant similarity is judged by a comparison of features of the cases. The system uses features of previously-decided cases to select similar cases from a case base, and displays the previous decisions to the user.
The system is intended to be used by researchers of AI and law, law students, and possibly junior legal practitioners. Junior legal practitioners and students or simply legal
Numerical taxonomy is the study of classification based on drawing inferences about similarity by the use of metrics established on the properties of the items that are to be classified. Chapter 1. Introduction 37 practitioners unpractised in the family-law area can learn by experience with ASHSD what legal information turns up in particular decision-making processes. ASHSD prompts its users with relevant question at different stages of its consultation process. After receiving their answers, the system uses these as input to its reasoning processes, and responds (after these processes have been completed) with decisions and justifications. This question- answering session and justification also helps the user to learn something about the relevant legal terms and issues with ASHSD’s cissistance. Thus, ASHSD can be viewed to some extent as a learning tool, although that is not the point of the thesis, and we have therefore done no experiments in educational psychology. But our main legal focus in building this system is to help the decision-making process. In family law, decision-makers need to use both rule-guided and Ccise-guided information, and ASHSD provides both of these to its users. In effect, it retrieves or determines possible outcomes, and shows information that can justify them. How to string such items together in a courtroom-quality narrative is up to the user.
Both RBR and CBR approaches have been used on legal examples by themselves or in a combined way. But previous research has not been concerned seriously with indicating which reasoning process is more suitable for a new case in an integrated environment. Our present research has shown that a human decision-maker can get acceptable information about the suitability of a particular reasoning method (e.g. RBR or CBR) for measures of similarity or respect to a current problem). A further benefit of ASHSD includes generation of partial suggestions for a new case when RBR has failed to give a clear answer.
The organisation of this thesis reflects the different aspects of ASHSD. The structure of the thesis is as follows:
1.7 Structure of the Thesis
Chapter 1 has provided the reader with the general idea of ASHSD and the research project. Chapter 1. Introduction 38
Chapter 2 makes a short survey of the previous related research work carried out on the use of computation in the legal decision-making process, including stand-alone rule- based systems, stand-alone case-based systems, and the combination of rule-based and case-based systems.
Chapter 3 describes the computational framework of the implemented system. It also discusses the concepts, objectives and methodological notions used in designing ASHSD.
In chapter 4, the knowledge representation scheme is presented. In chapter 5, the way in which ASHSD handles the issue of incomplete or fuzzy knowledge is described.
Chapter 6 covers the organisation of our legal knowledge base. The structure of the rule base is described in detail, and then its scoring mechanism for partial advice is discussed.
Previous research work on indexing and retrieval algorithms for CBR is reviewed, and a new indexing structure and a three-level retrieval mechanism are introduced. Chapter 7 describes our experiments with the test cases and their results.
Finally, chapter 8 sums up our approach to the research and considers possible future directions. Chapter 2
Previous Related Research
2.1 Introduction
A number of different systems have been used in the legal knowledge-based area. These systems can be divided broadly into three categories. One type of systems relies entirely on rules. Another category uses only cases. Finally, the third category combines both rule-based and case-based knowledge and is often known as a hybrid system. The main aim of this chapter is to review some of the best-known early projects in legal KBS.
The systems that only use rules are limited by their lack of power to reason about open- textured problems appearing in the contexts in which rule preconditions are evaluated.
Similarly, systems that use cases only are limited by their inherent weakness in reasoning about equivalence of cases with factual differences. There is a widespread recognition that rules and cases are complementary knowledge sources and both are necessary for legal problem-solving. Empirical studies have shown that in some domains integrating rules and cases can lead to improved accuracy (Mallory, 1989) and efficiency (Koton, 1988).
In the next section of this chapter, we shall examine closely the previously-designed legal systems and review the important issues in relation to those systems.
39 Chapter 2. Previous Related Research 40
2.2 Rule-Based Reasoning Systems
Rule-bcLsed representation and reasoning with knowledge is probably the most frequently used AI technique. Among other things, it gave the underlying support for the imple mentation of the earliest knowledge-based systems. Many researchers have attempted to formalise law in sets of rules using legal knowledge sources (e.g. legislation, codes, reports of previously-decided cases, etc.). Examples of this approach include: the work of Thorne
McCarty (McCarty, 1977); logic-programming models of legislation by a group at Imperial
College [e.g. (Sergot et al., 1986b), (Sergot et al., 1986a)]; Waterman and Peterson’s work on their so-called ‘Legal Decision-making System’ (Waterman & Peterson, 1984); the work on TAXADVISOR (Michaelsen, 1984); Richard Susskind’s work on Scottish law of divorce
[e.g. (Susskind, 1987b), (Susskind, 1987a)]; the development of the Latent Damage Ad visor by Capper and Susskind (Capper & Susskind, 1988). Now we are going to look at these research approaches.
2.2.1 TAXMAN I Project
The most well-known project on the application of AI techniques to law is Thorne Mc
Carty’s TAXMAN project at Rutgers University around 1972. The project was to study legal reasoning and argumentation, using US Income Tax law.
McCarty’s aims in the TAXMAN project are mainly theoretical. His early work on
TAXMAN is now referred to as TAXMAN 1 (McCarty, 1977). Later work also exists, e.g. on TAXMAN 11 (McCarty, 1982) and it is beyond the scope of only rule-based systems.
The US Internal Revenue Code classifies certain corporate transactions as tax-free reorganisation (of type B, C or D). The statutory rules and concepts which define such a reorganisation are represented in the construction of the TAXMAN I system. Therefore, the system can apply its definitions to determine whether a given corporate transaction would be classified as a tax-free reorganisation. TAXMAN 1 was originally implemented in a version of micro-PLANNER (Sussman et al., 1971). Chapter 2. Previous Related Research 41
The basic knowledge in TAXMAN I is represented by constructing a model of the relevant corporate tax law. The model consists of ‘objects’ like corporations, individuals, stocks, shares, securities, transactions etc. The idea of a template is used to represent each object class and a particular object is described by a template instance, which can be viewed as a collection of assertions representing the object’s properties (e.g. name, address, size etc). The domain is additionally defined by a system of relations which express the workable relationships between objects. The facts of a particular case are defined by a set of template instances and the relations between them (the templates contain variables which can be instantiated to have particular values). In this system, the templates represent the legal concepts defined by the statute (like ‘tax-free reorganisation’). The legislative rules which define these concepts are represented by logical templates, a term which is intended to suggest the way that templates for the high-level concepts are ‘matched’ to the lower-level fact descriptions during the analysis of a case.
Using the above knowledge, the TAXMAN I system is competent at analysing the tax consequences of a given corporate transaction. This is done by matching a logical template to the facts of the case and subsequently deciding whether the corresponding concept is true.
McCarty himself remarks that the knowledge representation of TAXMAN I is equiva lent to Horn-clause logic (McCarty, 1982), and that the reasoning is essentially deductive
(McCarty, 1983). The Imperial College logic programming group has developed (Schild,
1992) an equivalent to TAXMAN I , both in scope and functionality, using methods of logic programming.
McCarty’s TAXMAN I project is not intended to be commercially useful. Rather, it is striving to clarify the application of AI in legal reasoning.
2.2.2 British Nationality Act
Another important rule-based project is the British Nationality Act (Sergot et ah, 1986a).
Researchers at Imperial College, London, have formalised the British Nationality Act
(BNA). The basic tool used for this project was H orn Clauses as Prolog programs. The Chapter 2. Previous Related Research 42 key to their approach was the representation of knowledge by means of definite Horn
Clauses^ (Sergot, 1986). It was implemented in APES ^ [(Hammond & Sergot, 1983),
(Hammond & Sergot, 1984)], which is really an ‘augmented Prolog system’ and which allows the user to insert data in response to queries by the system. Thus APES utilises the same deduction mechanism used in Prolog - that of goal-directed pattern-matching.
The system also provides information to explain why a given query is generated and how a given solution has been obtained. For example, how a statutory provision may be transformed into the Prolog formalism (Sergot, 1986) is shown below:
Section 1(1) of the British Nationality Act 1981:
A person born in the United Kingdom after commencement [of the Act] shall be a
British Citizen if at the time of birth his father or mother is
(a) a British citizen; or
(b) settled in the United Kingdom
The logic representation is :
X acquires British citizenship on date Y by section 1.1
^For example, in a logic programming language one can write :
A : —Cl Cn
where A represents one single conclusion while C i On represent one or more conditions upon which the vahdity of that conclusion depends. This means that the conclusion (or goal) - A is true if the conditions
(or sub-goals) Ci....Cn are true. This form of representation is known as a Horn Clause.
^At Imperial College most applications have been implemented in APES which is an augmented form of Prolog. APES, like Prolog, works backwards from conclusions to conditions, and has the same fixed execution strategy. Chapter 2. Previous Related Research 43
IF X was born in UK
AND X WcLS born on date Y
AND Y is on or after commencement
AND Z is a parent of X
AND (Z is British citizen on date Y by section S
OR
Z is settled in UK on date Y)
In the above, X, Y, Z and S are variables to which the corresponding values, found as conclusions of rules and/or primitive assertions being searched, are ‘bound’. Once all clauses in the Act have been represented in the above manner, the system can search for every possibility that a query is satisfied, asking the user for information where necessary.
The clearest example of this principle is in an application to determine whether in a given case a particular individual, in a particular set of personal circumstances, is or is not a British citizen [(Sergot et al., 1986b), (Sergot et al., 1986a)]. The significant point here relates to ‘knowledge acquisition’, namely how ‘knowledge’ or ‘understanding’ is obtained from textual sources. They do not perceive that this poses a considerable obstacle, since their own method of tackling the law is described as follows
The formalisation of legislation by means of rules has almost all the charac
teristics of an expert system. It differs, however, in one important respect.
In a classical expert system, before knowledge can be formalised, it has to be
elicited from the subconscious of an expert. Eliciting this knowledge is gener
ally regarded as the main bottleneck in the construction of expert systems. It is
entirely absent, however, in the case of legislation which is already formulated
and written down. Thus the use of expert system techniques for represent
ing legislation has virtually all the advantages of expert systems without the
attendant disadvantages of eliciting the knowledge.
According to these researchers, this programming is entirely free from any problems Chapter 2. Previous Related Research 44 of knowledge acquisition, because legislation is ‘already formulated and written down’.
But it is an oversimplification of facts - because their programme cannot deal well with the transformation of the statutes into the IF -
Certain other artificial intelligence and law researchers [e.g. (Leith, 1986), (Moles,
1991), (Moles & Dayal, 1992)] have also criticised the use of logic programming in the law, not from the computer science perspective but from a position outside the usual domains of computing. In this regard Philip Leith claims that the normalisation of legislation is not as simple as the Imperial College team has suggested, and that it is a very mentally de manding task which requires considerable interpretion of legislation. The same researcher argues:
The problems of logic programmers are the result of a false epistemology: they
see the world in terms of a computational model and fail to stand outside that
model. Thus, whenever they attempt to apply their model to the real world,
they will always fail. [(Leith, 1986), pp.552]
BNA is rather pragmatic in orientation. The immediate application potential of AI
^LEXIS is a well-established on-line legal document retrieval system provided by Mead Data of Dayton, Ohio, in co-operation with Butterworth Telepublishing in London.
^For example, a typical question posed by the system is whether the person in question has ever
‘renounced’ British citizenship. There may be a variety of different ways of renouncing citizenship, each of which is highly context-dependent. In some cases, determining whether there hcis been renunciation may be the most complex part of the aneilysis of citizenship. Chapter 2. Previous Related Research 45 for the law can be recognised more easily in their work than in McCarty’s TAXMAN project. However, BNA’s work is also orientated towards AI purism and is generally concerned with identifying the weaknesses and strengths of logic programming techniques rather than trying to imitate exactly what lawyers do in reaching their (hopefully logically reliable) conclusions. Therefore criticisms like those above are fair, but the Imperical
College researchers may say that they do not affect the main AI dimension or emphasis of their study,
2.2.3 The Oxford Project
The Oxford Project [(Gold & Susskind, 1986), (Susskind, 1987b), (Susskind, 1987a)] was initiated at Oxford University, It was a collaborative research project which developed a
rule-based language for representing certain kinds of law. Susskind has illustrated the use of this language with an example based on Scottish divorce law. The Oxford Project was focused on analytical jurisprudence (i,e, legal philosophy legal theory), which is that part of legal theory that seeks to promote a systematic, theoretical and general understanding of issues such as the structure of legal rules and legal systems, the nature of legal reasoning, the role of logic in the law, and the interpretation of legislation and case law.
According to Susskind (Susskind, 1987c) the Oxford Project addressed many issues within analytical jurisprudence in so far as they were deemed to have implications for
building expert systems in law :
1, the nature of legal knowledge
2, the activity of legal science
3, the individuation of laws
4, the structure of rules
5, the nature of legal systems and legal sub-systems
6, the nature of legal reasoning; and
7, the relationship of logic to the law. Chapter 2. Previous Related Research 46
The knowledge of this project was selected mainly from statutes, case law, and stan dard legal textbooks. The knowledge was represented in a knowledge base as a network of interrelated rules, and the inference mechanism was a backward-chaining reasoning process.
There are many conflicting theories and schools of thought within the jurisprudential literature. The Oxford Project identified a (limited) consensus over many relevant ques tions of jurisprudence and demonstrated that expert systems in law both could and should be developed on the basis of that body of agreement.
2.2.4 LDS and SAL Systems
Legal Decision-making System (LDS) [(Waterman & Peterson, 1981), (Peterson & Wa terman, 1985)] is an example of a rule-based system. LDS is a system developed by Don
Waterman and Mark Peterson which addresses the problem of settling product liability cases. This system helps to determine the settlement value of personal injury cases and is implemented in ROSIE^. In LDS the knowledge, such as the strategies used by legal professionals, is represented in both a formal (based on statues and cases) and an informal way. The main aim of LDS was to develop models of the actual decision-making processes of legal professionals, using different kinds of knowledge. This description is again evident in another rule-based system, SAL (System for Asbestos Litigation), to assist legal profes sionals in evaluating claims relating to asbestos exposure (Waterman & Peterson, 1986).
SAL is also implemented in ROSIE.
LDS and SAL differ from systems that seek to perform legal analysis. These systems showed how to model legal skills and expertise. Their primary goal was to develop models of the actual decision-making processes of legal professionals and not legal analysis.
^ROSIE (Rule-Oriented System for Implementing Expertise) is a programming environment for building expert systems. Chapter 2. Previous Related Research 47
2.2.5 TAXADVISOR
The TAXADVISOR Wcls designed and implemented by Robert Michaelsen [(Michaelsen,
1982), (Michaelsen, 1984)] to assist legal professionals with tax planning for clients with large estates (valued at more than $175,000). The system was implemented in EMYCIN®.
It gathers information about clients and makes planning recommendations and gives advice to improve the client’s tax position.
The knowledge represented in TAXADVISOR comes primarily from legal professionals’ experience and the strategies they use in practice. The researcher validated his system by having it examined by several practising accountants. This system is an example of how legal knowledge can be used in real expert systems.
It is another example of a system that has concentrated on modelling, not so much the law itself, but rather how a knowledge of the law is actually used in practice.
2.2.6 Latent Damage System
Richard Susskind and his colleagues have designed and implemented a legal expert system
(Capper & Susskind, 1988) based on UK’s Latent Damage Act 1986. The system advises legal professionals on latent damage, and related law of contract, tort and product liability.
Broadly, there is latent damage when some damage occurs, yet knowledge of that damage only arises some time later. ‘Damage ’ in this context means both physical damage and economic loss caused by negligent professional advice (e.g. by solicitors or accountants).
The physical damage has important effects for the manufacturing, construction and supply industries. The crucial issue here is whether a sufficient period of time has elapsed since the damage was sustained such that any right of action for negligence action is lost.
The Latent Damage System provides advice on latent damage law with the help of its own store of knowledge. The key advice offered by the system is the date after which the
TMYCIN (Essential MYCIN) is a rule-based expert-system language. Chapter 2. Previous Related Research 48 claimant can no longer commence proceedings. The system offers user friendly prompts, help and explanation facilities.
2.2.7 The Use of Meta-rules in Rule-Based Legal Computer Systems
Rule-based systems in the legal domain are often obtained by formalising legislation. Many legal researchers (Schild & Herzog, 1993) consider the addition of meta-knowledge in the form of meta-rules to such a system. Actually the meta-rule approach is important for dealing with open-textured problems in jurisprudence. Law is characterised as the union of primary rules of obligation with secondary rules (Hart, 1961). The secondary rules are
about the primary rules, and these secondary rules are the meta-rules.
2.2.8 Commentary on RBR Systems
The intention of the systems discussed above is mainly to make a computer model of a
particular set of associated legal rules. The drawback of such systems is that whenever
the condition part of a rule contains an open-textured term, the system must simply
ask the user whether the meaning of the term is fulfilled. These systems fail to express
rules adequate to find whether open-textured terms are actually true or fulfilled in the
descriptions of new cases. Therefore, the task addressed by these systems is a simplification
of real legal reasoning.
For example, the BNA research effort pointed out that statutory rules can often be
represented as Horn Clauses with only little changes of the original statutory language.
However, the BNA system is incapable of reasoning about whether open-textured terms
appearing in statutory rules are fulfilled by the particular facts of a given case. Alterna
tively, the system relies upon the user to make this determination. Similarly, the system
LDS forced onto the user the task of determining whether open-textured terms were ful
filled.
Many areas of law use automated systems designed around legislative law and con- Chapter 2. Previous Related Research 49 cerned with legal analysis. Researchers have tried several methods in this regard. One popular method involves formalisation by transposing the apparent meaning of legal in formation in natural language to a logic-based language such as Prolog. One of the dis advantages of this method is the deficiency of considering a specific piece of legislation in isolation from the rest of the body of law. As a result, much external knowledge is needed if fully reliable interpretation is to take place. Another approach would be to provide for heuristic knowledge about how to understand pieces of legislation in a particular domain and their use in a particular decision-making situation. Such knowledge would have to be provided by experts. Also, the view of an item of legislation that is represented in such a knowledge base is limited, and open queries cannot be answered in an authoritative way.
However, legal RBR systems have several problems: [i] elicitation of knowledge is a difficult task, often being referred to as the knowledge elicitation bottleneck; [ii] RBR may not properly reflect the reasoning structure used by the experts; and [iii] the open-textured nature of legal knowledge and reasoning is difficult to capture. In summary, a stand-alone rule base is in general not adequate for the legal decision-making process.
2.3 Case-Based Reasoning Systems
Particularly in legal reasoning, each rule may have many exceptions. In addition to this, in certain circumstances (where the situation is not so clear) legal experts have often relied on previously-decided cases which are similar to the new case. This reasoning process is known as CBR. The previously-decided cases can bridge at least some of the above mentioned problems in the RBR systems. This is because the facts of the previously- decided cases, like those of new cases, are expressed in the case-description language. We shall now give a brief review of the early CBR systems.
2.3.1 JUDGE
JUDGE [(Bain, 1984), (Bain, 1986), (Bain, 1989)] has been developed to assess cases and select sentences for minor criminals. The domain of this system is criminal sentencing
LONDIN. Chapter 2. Previous Related Research 50 in assault and manslaughter cases. The system takes as input a description of a fighting situation that resulted in death, and where the offender must be sentenced. Crimes are expressed in terms of the actions of the parties, e.g. who started the fight, how violent it was, fear factors and the chances of the offender repeating his violent action, etc.
The system assesses the situation and then adapts a solution from a previous similar case to find a sentence in the new case. After finding a sentence, JUDGE tests its consis tency by finding a sentence from a second case (either the second-best match or the case from which the retrieved case was derived) to make sure the sentence is consistent with other previous decisions. The following steps of reasoning are used by JUDGE :
• The facts and interpretation of facts in the case;
• Sentences from similar previously-handled cases;
• Rules generated from previous cases; and
• Rules based on statutory requirements.
JUDGE obtains a series of facts and has goals (represented as rules) in order to decide what interpretation to place on each of the facts. According to Bain, these goals are the
'features which are significant to human judges ... including the extent of harm suffered
by the victim, interpretations which both explain a person’s actions, such as ‘self-defence’,
and which suggest a degree to which an action was justifiable, and the relative importance
of mediating circumstances.’ (Bain, 1986).
After an initial interpretation, an investigation is made of those cases and sentencing rules that are similar to the current case under consideration. When two or more similar cases arise, a sentencing rule is created using a template of the common parts of the interpretations in those cases for future use. A modification in the rule may be made in the light of some new case.
The severity of crime is compared among similar cases found in the memory and the rules are then applied. Reasoning goals are used to compare the relative severity of the new crime versus the one in memory and then the sentence is adjusted depending on this Chapter 2. Previous Related Research 51 comparative study. In the event that there are no similar precedents, JUDGE uses a set of rules derived from statutory law or otherwise. These rules are used as guidelines with refinement provided by ‘experience’.
Bain showed how a CBR system could use a machine learning technique to learn from experience using JUDGE, and the goals then would be used to direct reasoning. New cases
would cause changes in the system’s reasoning process by a modification in the sentencing
rules used. This reasoning process is controlled by goals that assign relative importance to the different facts.
2.3.2 HYPO
HYPO [(Ashley, 1987), (Rissland & Skalak, 1989c)] and(Rissland & Ashley, 1989) is an example of a CBR system in the domain of trade-secret law. In trade-secret legal cases the plaintiff and defendant are often two corporations producing competing products.
The secret trade information of one corporation will sometimes be obtained by the rival corporations in an illegal manner: for example, by theft or through a former employee of
the plaintiff corporation. A typical way for the plaintiff to argue his case is to show that
the purported secret enables the defendant to gain an unfair competitive advantage.
The main aim of HYPO is produce arguments and identify issues that may be used
by a legal practitioner to reach a decision. Ashley and Rissland leave the actual decision
making to the human decision-maker of the system.
A relatively simple representation of the cases is used. The idea of similarity between
cases is restricted to features that are important on the surface. HYPO’s indices are called
dimensions. Each dimension corresponds roughly to one of the ‘factors’ that decide the
outcome of a case. In addition, each of HYPO’s dimensions contains a set of conditions
which specify when the dimension applies in a given case. Indexing is done by means of
thirteen dimensions.
A case is represented as a frame by means of some salient features, arranged according
to an expert’s understanding of how relevant particular features are to the conclusion (pro Chapter 2. Previous Related Research 52 plaintiff or pro-defendant) in the case and how minor variations can affect the features’ ability to influence the conclusion. When HYPO receives a new case, it can generate a
3-ply argument as follows:
1. (Citation) HYPO selects the most appropriate cases that support the new case and
are most similar to it.
2. (Responding) HYPO picks up such cases in terms of uncommon dimensions and
selects more appropriate counter-examples in the claim lattice.
3. (Rebuttal) HYPO rebuts the response by distinguishing the cases again. It is done
by weighting the dimensions according to explicit rules in several steps.
The reasoning by which the decision in the case was reached is not represented explic itly. As the developers admit, to do so would require a significant expansion of HYPO’s reasoning techniques (Rissland & Skalak, 1989c).
In summary, HYPO represents a detailed model of how different types of arguments can be built using only knowledge of the factors that support or negate a classification and the magnitude of the factors in a set of predicates and new cases.
2.3.3 The Trademark Reasoning and Retrieval System (TR2S)
TR2S is a CBR system for legal experts (Huang & Cross, 1989). The system was designed to reason about trademark infringement cases, a restricted domain, in the law relating to aural or visual confusion between trade marks. A formal language (RDL - Relational
Description Language) was used for describing trademark infringement cases and the ar guments presented in court decisions. A simple argument structure is used, broken down into (i) who proposed the argument, (ii) the belief or assertion to be established by the argument, and (iii) the reasons advanced in favour of the belief. Relevance of one case to the issues arising in another depends only on the following: Chapter 2. Previous Related Research 53
• the party’s trademarked goods are in comparison in relation to their business type,
i.e. whether they are in same type of business or not;
• how goods are related with the respective trademarks; and
• the relationships between sound, appearance and meaning of the respective trade
m arks.
A small number of indices are adequate in this representation formalism. Using these indices the arguments are computationally represented to establish similarity between cases. At the application level, it is not definite when there are disagreements in argument and there is little guidance from the court as to which issues were operative. Therefore, the builders have to abstract the necessary argument from the case reports alone. It is evidently hard to avoid imposing the personal interpretation of the domain expert at the time of the abstraction process on the legal material. This is not by itself intolerable, but the users must be fully aware of the significance of this point.
2.3.4 CHASER
CHASER (Cuthill, 1993) is a CBR system which uses a multi-layered case representation
approach to reason about tort law cases. CHASER also addresses the problem of modelling case-based reasoning with cross-context reminding in the legal reasoning.
CHASER accepts descriptions of new fact situations in a predicate-calculus form and
provides a description of the possible legal cases that can result from that situation and the closest past legal cases for resolving issues in that situation. A legal case derived from a new fact situation consists of the arguments each side in the case can make: the cause-of-action and defence. The cause-of-action consists of a duty construct, the event which breached that duty, the event that illustrates some harm to the defendant and the causal chain that connects the breach and harm events. The cause-of-action and defence generate the issue or specific point on which the two parties in the case disagree. This disagreement has some basis in the facts (or interpretation of the facts) of the case. Chapter 2. Previous Related Research 54
Once CHASER has analysed the new situation and selected the issues, it finds the most useful, or closest, past cases for resolving the issue present in the new case. These cases are those that addressed the same issue and derived that issue in as a similar a manner as possible to that of the new case. Therefore, the indices used for finding the closest past cases for resolving the issue in the new situation are the type of issue, defence, cause-of-action, duty and disputed event in the new case. CHASER provides the past case and a description of how that past case is similar to the present case and why the past case resolved the issue in the manner it did.
2.3.5 Commentary on CBR Systems
The main criterion for evaluation of any CBR system is the effectiveness of its retrieval of the relevant cases. Therefore, the similarity assessment in CBR is of central importance.
We have seen in the above discussion that different researchers have adopted different methods in determining the similarity between precedent and a new case. We shall consider two of these methods, ‘weight feature matching’ and ‘dimensional approach’^ which are relevant to our research.
In th e weight feature matching method, the similarity between cases is measured as a function of common features, or of the features on which they differ (Stanfill & Waltz,
1986). The precedent with highest weighting is then considered for the new case. One of the main disadvantages of this method is that it is hard to find out a set of feature weights that is a guide to the right matching behaviour (Murphy & Medin, 1985). This approach is also inadequate for representing interaction among features.
The dimensional (Ashley, 1987) approach uses features that help to establish a con clusion or its negation. In this method, the context-independent similarity metric is not required. In the dimensional approach, the similarities between two caaes are judged by common dimensions and the differences are judged by dimensions that the cases do not have in common. A precedent is more similar to a new case than other cases if the old case shares more dimensions.
The above mentioned stand-alone CBR systems are not comprehensive legal reasoning Chapter 2. Previous Related Research 55 systems. They have shown how to use previously-decided case reports effectively to find a decision for a new case. The drawback of these systems is that they use only the previously- decided case reports without giving serious consideration to other legal knowledge sources
(e.g. legislative information). The key motivation of these researchers was to simulate aspects of legal reasoning using only the CBR approach and excluded the important aspect of legal reasoning which requires use of both RBR and CBR (Pal & Campbell, 1995).
2.4 Hybrid Legal Knowledge-Based Systems
The drawbacks of stand-alone rule-based and case-based reasoning have been examined in the previous sections. The current section surveys the characteristics of hybrid systems, which can reason with both rules and cases. The following discussion emphasises the early hybrid systems in the legal area.
2.4.1 TAXMAN II
McCarty’s TAXMAN I can be exemplified as a deductive RBR system expressed in terms of a structured representation of the underlying domain. TAXMAN II [(McCarty & Srid- haran, 1982), (McCarty & Sridharan, 1980), (McCarty, 1989)] builds on this earlier work.
CBR is only useful, however, if either there are enough precedents that every new case is identical to some precedent or there is enough matching knowledge that every new case can be matched to some precedent in the group to which it belongs. Thus there is a trade-off between the quantity of matching knowledge and the minimum number of
precedents require for effective CBR systems (Porter et al., 1990). Only a small number of
precedents is enough when the matching knowledge is extensive. This approach is known as th e prototype approach. The prototype approach to CBR is exemplified by TAXMAN
II (McCarty & Sridharan, 1982).
There are two major parts of investigation which McCarty has been pursuing in TAX
MAN II. The first of these addresses specifically the treatment of open texture. It proposes Chapter 2. Previous Related Research 56 a structure called prototypes-plus-deformations. The second part of the investigation is the development of reasoning with deontic concepts (obligation, permission and prohibition).
It relies on the idea that a legal concept whose meaning has developed with the collection of case law can be represented in terms of a structure with three components.
1. There is an invariant ‘core’ which expresses necessary but not sufficient conditions
for the concept to hold. This component can be optional.
2. There is a set of exemplars, each of which matches some but not all instances of the
concept.
3. There is a set of transformations that express the relationship between the exemplars
by stating how one exemplar can be mapped into another.
A legal argument for a specific classification is modelled as a set of conversions that includes all the exemplars and the given case. A counter-argument consists of a set of conversions that includes the exemplar but excludes the given case. The most convinc ing argument is the one that ‘establishes the greatest degree of coherence on the set of exemplars’. It is worth observing that a particular structure-matching followed by match enhancement would perhaps constitute a conversion under the TAXMAN II scheme.
2.4.2 Gardner’s System
Many hybrid systems are incapable of achieving rule-based reasoning to assist in matching.
These systems can achieve goal re-formation, but not case elaboration. Gardner has published (Gardner, 1984) one of the most complete accounts of legal reasoning, which distinguishes it from previous approaches. She address directly the treatment of open texture and the related use of CBR to complement reasoning with rules.
The central feature of the implemented system was to spot the issues upon which the outcome of a contract case would depend and to distinguish the ‘hard’ ones from the
‘easy’ ones. Gardner has emphasised that most legal questions do not have a single ‘right’ answer (until they have been decided in court). The system produces as output a tree Chapter 2. Previous Related Research 57 of all possible solutions that can be reached for a given case. Branches in this tree are introduced by the presence of open-textured concepts which cannot be decided definitely one way or the other. This tree of possible solutions corresponds to a set of conditional answers, where each answer is qualified by the open-textured conditions that remain to be determined.
The implemented system tries to find out which of the open-textured conditions are
‘easy’ and which are ‘hard’ (the ‘issues’). Gardner uses different rules to capture the notion of ‘hard’ and these rules are marked as competing. On encountering a condition that hcLS several competing definitions, the system must try every competing rule. When the competing rules do not agree in their conclusions, then the legal question is categorised as
‘hard’, and the condition becomes one of the qualifications on the final conclusion reached.
But when all the competing rules agree, then the condition is taken as decided and does not become a qualification on the final conclusion and the legal question is categorised as
‘easy’. Gardner suggested a number of other heuristics that could be used in addition to distinguish ‘hard’ from ‘easy’ conditions.
By allowing RBR to precede CBR, Gardner’s system performed goal re-formulation.
However, the system lacked any provision for case elaboration: precedent cases applied only to new cases that matched them exactly. Moreover, Gardner’s system had no ways to generate competing arguments on either side of a given question.
2.4.3 GREBE
GREBE [(Branting, 1988), (Branting, 1991)], standing for GeneratoR of Exemplar-Based
Explanations, is a hybrid system in the domain of Texas worker’s compensation law. It uses exhaustive knowledge of the facts and the reasoning of particular past cases, integra tion with legal rules and common-sense knowledge to determine and establish the legal outcomes of new cases. GREBE uses detailed knowledge of the facts and explanations of precedents to find the similarity between precedent and the new cases. It can use either
RBR or CBR to reach goals at any level of its legal analysis. A choice between RBR and
CBR is measured on the basis of the strength of the explanations that result from each Chapter 2. Previous Related Research 58 alternative.
g r e b e ’s rule-based reasoner is a Horn-clause resolution theorem-prover similar to
Prolog and its case-based reasoner consists of three components; a precedent retriever, a structure mapper and a match improver. The precedent retriever locates the instance and noninstance precedents of a given predicate whose critical facts most closely match those of the new case. Next, the structure mapper determines the best mapping from the critical facts to the new case. The match improver then attempts to infer any facts in the new case that would improve the match. GREBE uses a method of CBR in which new cases are compared with the smallest collections of precedent facts. The precedent retrieval process uses three steps. The structure mapper is used first, the match improver second, and the precedent retriever last. GREBE’s output is a memorandum that states a possible legal outcome in terms of the applicable precedents and legal rules.
2.4.4 CABARET
CABARET (Rissland & Skalak, 1989b), standing for CAse BAsed REasoning Tool, is a hybrid system which integrates RBR and CBR. The integration of RBR and GBR methods is performed by using control heuristics. These control heuristics suggest how to interleave
RBR and CBR to produce an argument to support a certain interpretation. CABARET was implemented in the application area of US federal income tax law concerning the deduction from expenses relating to an office maintained in one’s home. Since the CBR component of the project is of the HYPO variety, CABARET uses the same case repre sentation through feature-value pairs as HYPO. The RBR component uses statutes and regulations and is a standard production system.
Open-textured terms refined through use in past cases form the substance of statutes and regulations. If CABARET determines that a new fact situation meets all but one of the requirements of a rule, it uses its CBR component to find the closest precedents that had the same features as the new case. It then groups the precedents into those that allow and those that prohibit deduction. CABARET has the following basic features: Chapter 2. Previous Related Research 59
1. There are two primary knowledge sources (co-reasoners): a HYPO-style CBR
component and a traditional RBR component. These co-reasoners are capable of
running separately in a stand-alone manner.
2. Each co-reasoner has a dedicated m onitor. The function of these monitors is to
observe the progress toward a solution. This includes goal satisfaction and certain
intermediate problem-solving states. Internal description is in a language known as
‘Control Description Language’ (CDL) understandable to the controller.
3. Observations are reported to a controller process that uses the monitors’ obser
vations to determine how the system as a whole and the individual process are to
continue. This decision is based on the controller’s set of control heuristics^ which
are also encoded in the CDL.
The overall behaviour of CABARET can be described in the following steps:
1. The user inputs a case, and an overall goal for the system.
2. It analyses the fact situation by using rules and cases. The system reasons op
portunistically and creates a trace of reasoning tasks relating the applicability and
suitability of known rules and cases. CABARET interleaves CBR and RBR dynam
ically.
3. The system then produces an argument or explanation from the desired point of
view as to why a certain interpretation should or should not hold, complete with
case-based and rule-based information that may include both advantages and disad
vantages.
Both GREBE and CABARET use a combination of case-based and rule-based reason ing to justify a decision on the applicability of a legal principle to a case. However, the specific goals of case analyses in this project are not addressed by these two systems. The goal here is a case analyser that determines the domain information applicable to a case derivable from the new situation in a manner consistent with domain requirements. Chapter 2. Previous Related Research 60
2.4.5 PROLEXS
PROLEXS (Walker et al., 1989) is a hybrid legal reasoning system developed at Vrije
University of Amsterdam. The system is able to give legal advice in the area of Dutch landlord and tenant law. PROLEXS uses several structurally different knowledge sources, which are called knowledge groups. Each knowledge group has its own knowledge repre sentation language and dedicated inference engine. In PROLEXS four knowledge groups were used: legislation, legal doctrine, expert knowledge and previously-decided case re ports. For example, the legislation knowledge group is represented by a rule-based system and its inference engine comprises both forward and backward chaining methods. The previously-decided case inference engine is a simple case-based retrieval and reasoning method. Possibly the easiest way of representing cases in a form suitable for a computer is to decide on all the features that describe a case on a specific subject and attach numerical weights to each of the features in proportion to their usefulness in influencing a particular legal consequence. The new case which is to be reasoned about can also be represented in the same manner. When a precedent case and a new case are being considered for similarity, the total weights accorded to each are compared. If the weight attained by the
problem case equals or exceeds the total weight of the features of the precedent, it may
be inferred that the outcome in the problem case ought to be the same as that in the
precedent.
The inference methods of the independent knowledge groups interact with each other
by using a agenda-based blackboard architecture. Decisions drawn by one knowledge group are written on the blackboard in such a way that it is readable to all inference engines.
Then th eagenda-controller decides which reasoning method will go forward. This selection
procedure takes into account feedback from the reasoning methods themselves. The feed
back, known as report, tells the agenda-controller a number of things. First, it specifies whether the reasoning method has been successful in deducing new facts or even reaching the user-supplied goals. Secondly, it states which part of the knowledge base the reasoning
method is planning to use next; and finally it calculates the expected workload should the reasoning method receive permission to continue. Chapter 2. Previous Related Research 61
2.4.6 IKBALS II
IKBALS II (Vossos et al., 1991) works in the area of Australian accident compensation law and assists a legal practitioner in building a favourable argument for a client. Its hybrid reasoning module combines rule-based reasoning and case-based reasoning to determine if an injured employee is entitled to compensation. A worker is entitled to compensation only if the Accident Compensation Commission is satisfied that the worker falls within certain statutory definitions. IKBALS II uses three types of domain knowledge:
• the previously-decided case reports (i.e. precedents);
• the statutes and regulations;
• expert heuristic knowledge needed to reason with both the precedents and legislation.
IKBALS II helps the legal practitioners to assess the mérités of a new case by using the precedents and legal experts’ specific domain knowledge. The system helps the user to decide the likely outcome for a new case by comparing it with the stored precedents.
In doing this it uses the facts for the precedents that the court identified as significant in determining the case. The system takes into account the nature of the injury, the infor mation obtained from the worker and medical practitioner concerning the circumstances of the injury, and the degree of incapacity.
2.4.7 HELIC II
The HELIC-II system was developed by Nitta (Nitta et al., 1992) as a hybrid approach to legal reasoning in the domain of criminal law. It combines rule-based and case-based reasoning in one model.
HELIC-II is based on logic programming and implemented on the parallel computer
PIM (Parallel Inference Machine). It is made up of two inference modules - a rule-base module and a case-base module. The rule-base module refers to the rule base which contains legal rules in the form of logical formulas and draws legal conclusions by applying Chapter 2. Previous Related Research 62 rules deductively. The case-base module refers to the case base which stores old cases in the form of case rules, and generates legal hypotheses by similarity-based matching.
HELIC-II produces a set of arguments where each argument is an inference tree. The
root of this inference tree is the conclusion and the leaves are the initial facts of a new case.
Certain arguments are based on the plaintiff’s (or prosecutor’s) opinions of old cases and other arguments are based on the defendant’s opinions. Also, no priority is assigned to the
result arguments. Given a new case, HELIC-II generates all possible legal consequences
and their explanations by referring to old cases and the penal code,
HELIC-II presents explanations of old cases by semantic networks and case rules with
the reasoning executed by partial matching of semantic networks. This method is similar
to the reasoning mechanism of GREBE which also draws conclusions by rule-based and
case-based reasoning. The inference mechanism of HELIC-II was made simpler than that
of GREBE to obtain high-performance parallel inference,
HELIC-II and CABARET are hybrid systems that consist of two inference engines,
CABARET has a controller that controls a rule-based module and a case-based module.
A controller has heuristics to manage reasoning steps, and complex control is realised. In
the case of HELIC-II there is no controller because HELIC-II was developed on a parallel
computer (PIM) where the two inference engines operate in parallel. The final inference
trees are constructed in an ‘explanation constructor’,
2.4.8 Conclusions
All the research projects described above have used a RBR or a CBR or a combination-of-
both approaches. It appears that both formalisms have their place within the framework
of legal automated systems. Previous work in the legal reasoning area has helped identify
the following as important additional goals for research:
• Choice of a good multi-layered case representation method to support reasoning
about law (Cuthill, 1993). Chapter 2. Previous Related Research 63
• Combining rule-based and precedent-based reasoning for applying legal principles to
cases [e.g. (Branting, 1991), (Rissland & Skalak, 1989b), (Rissland, 1990)].
Severl legal knowledge-based systems have been constructed by exploring various ap proaches to the combination of RBR and CBR. ASHSD resembles CABARET, PROLEXS and IKBALS II in its ability to use both of RBR and CBR. However, ASHSD differs from these systems in its control structures. The user has the option of selecting the reasoning method in either order of preference (CBR followed by RBR or vice versa) or to express no preference at all. In this context, ASHSD has the simplest control structure. When the user indicates no particular preference of reasoning (RBR or CBR), ASHSD applies each method separately, and presents its results based on an automated relative rating of the
RBR (based on second type of rules, i.e. prediction rules) and CBR scores. The relevant similarity is judged by matching the features of the selected best case and best rule with the new case. The relative rating involves use of a normalisation of rule scores against case scores, which has obtained by experiments and validation by further trials, as discussed latter in this thesis. We are interested in determining whether our simple control structure is capable of producing useful results. If it is, then it should be of interest to people who have both rule bases and case bases for some particular area of knowledge and want to use them together but (for whatever reason) do not want to do any significant re-engineering or rebuilding of these knowledge bases. Typically, the more complicated architectures we have reviewed above would require the knowledge bases to be re-engineered in some way. Chapter 3
Framework of the System
3.1 Introduction
The main emphasis of this chapter is on the inner environment of ASHSD. It addresses two sets of issues, namely:
1. The concepts behind ASHSD, and hence the characteristics and the objectives of
the system; and
2, Some methodological notions which have been used in designing ASHSD.
In dealing with these issues, this chapter will attem pt to assess the most critical notions in relation to the special characteristics of ASHSD. Following this, the next section will focus on the development of a knowledge base that is suitable for the present research.
64 Chapter 3. Framework of the System 65
3.2 Concepts behind ASHSD
3.2.1 Characterisation
We should first clarify the term ASHSD in relation to the notions used in this research project. Let us give a brief characterisation and then eliminate some possible misunder standings. The term ASHSD was chosen because it expresses the notion that the system is intended to be advising and supportive in nature and to help in divorce settlement.
ASHSD picks up the relevant facts of a new case from a question-and-answer session with the user, and then generates its conclusion through an interaction between the user and its own stock of information about the law.
3.2.2 Objectives for the System
At the beginning of the project, the basic aim behind the development of ASHSD was to test an hypothesis about a simple way to combine the use of rule-based and case- based knowledge. Its effectiveness was to be tested in a particular (legal) area, by seeing to what extent ASHSD could provide a readily-accessible source of relevant advice for certain classes of people concerned with decision-making regarding matrimonial home settlement in English divorce cases. Particularly the system was intended to have some use among researchers of AI and law, law students, and legal practitioners. We did not expect that this use would extend to ASHSD being a full-fledged learning tool, because this would need more supporting materials than ASHSD possesses. For example, on-line legal dictionary facility, current statutory information and other materials would be required for a undergraduate law student to learn a new area of law from it. But for all our expected legal users, we would expect ASHSD to provide guidance and assistance for education and decision-making. It was recognised that the system would not act as a substitute for professional advice, i.e. that a human expert must be responsible ultimately for making the final decision, no matter how straightforward and easy a particular case might seem.
ASHSD’s guidance is rather more passive than active; it generates suggestions for the user to think about. Even though it is a somewhat basic system, paradoxically it may even be Chapter 3. Framework of the System 66 more helpful to users whose competence has progressed beyond the basic (because such people can apply more contextual knowledge to the interpretation of ASHSD’s outputs) than to near-beginners in legal studies. However, we have had no opportunity so far to test that speculation.
For ASHSD to be of practical use, it should be implemented on hardware and software that is widely available. As a result the preferred delivery vehicle chosen was a stand-alone personal computer, using LISP^.
3.3 ASHSD’s Knowledge Base Development
ASHSD’s KBS development follows a methodology that consisted of the following four distinct phases:
1. M odel Building;
2. Knowledge Acquisition;
3. Prototype Development: Incremental Development of Full System; and
4. Testing and User Trials.
which are described in detail following an overview of the application domain.
3.4 The Domain: Matrimonial-Home-Related Orders
Usually the major asset owned by the couple in a marriage is their home. This represents not only capital investment but also a roof over their heads. Disputes about the matri monial home are more common than disputes about the divorce itself (Cretney, 1992).
^LISP is a programming language designed to handle symbolic computation and represents both pro gram cind data in list form. Chapter 3. Framework of the System 67
The Matrimonial Home Act (MHA) 1983 is one of the controlling statutes in this area.
The other resource commonly used is previously-decided case reports. These can be used to discover the principles underlying matrimonial home settlement cases, and to embody these principles in a computational framework facilitating the final decision in similar cases.
In the case of broken marriage, the court has complete discretion as to how it divides up the property or acts to protect any of the members of the divorced family. The court’s main concern will be for the welfare of the dependent children of the marriage (Black &
Bridge, 1992). The court’s order will depend upon whether the home is owned or rented.
Matrimonial-home-related orders can be any of the following:
[A] Owned M atrim onial Home: An owned matrimonial home, in practical terms,
usually falls into one of three categories for disposition:
[i] Transfer the m atrim onial home to one of the spouses. For example, if
there are dependent children from the marriage and they are living with one of
the spouses, the court might give the matrimonial home totally to the person
receiving custody of the children.
[ii] Sell the m atrim onial home and divide the money. This may well be the
best solution if neither can afford to keep the house or flat. Often it is agreed
to sell the house so that the proceeds can be used to enable the wife to buy a
cheaper house with the husband giving her a larger than normal share of the
proceeds in return for a reduced (or no) maintenance entitlement.
[ill] Keep the matrimonial home for the spouse who is taking care of the
dependent children. This is the order that is very often made if there are
young children. Indeed, it is often the only feasible solution. The typical order
will then be for the wife to live in the house with the children, and for the house
to be sold when the youngest child reaches 18. This type of order is known as
a Mesher order.
[B] R ented Property: The divorce court can order that the tenancy be transferred to
one of the spouses. Chapter 3. Framework of the System 68
[i] Privately Rented Property; The position depends on the terms of the
tenancy, and whether there is any restriction on the tenancy being transferred.
[ii] Council Property: The council will transfer the tenancy from one tenant to
another only if a court order has been made. The court can make an order
only on divorce or judicial separation. The position will therefore depend upon
whether one or both of the spouses are tenants.
[C] Injunction: Often in the case of marital breakdown, violence can start between
the spouses and may lead to an appeal for an injunction. Furthermore, behaviour
falling short of violence may cause distress too. The power of the courts to protect
members of the family from violence or other distressing behaviour at the hands
of another member of the family must be considered. The injunction, or an order
equivalent to it, is the most effective civil remedy provided by the law.
Injunctions restraining offending behaviour can be granted in appropriate cases. In some cases, the courts have to accept that an injunction restraining the use of the offending behaviour will not be sufficient protection. Families living under the same roof while a relationship is breaking down are subjected to great pressures of all kinds, pressures that will not always be controlled or alleviated by injunctions. Often such an injunction can add to the pressures. In such cases, there may be no option but to make an order whose effect is to separate the parties, an order that ousts one of them from the home. This is often known as an exclusion order. It can have many variations: for example, it may be sufficient to prevent one party from using certain rooms in the home, an ouster from part; or it may be necessary to provide that a party must leave the home.
While commonly it is the case that these orders are sought by one party because of behaviour directed at them by the other, it should be appreciated that these orders can also be used to protect children in the family, when they are being subjected to offending behaviour. Chapter 3. Framework of the System 69
3.5 Methodology
The development methodology of ASHSD consists of the following four phases:
[1] M odel Building;
This phase is concerned with hierarchical decomposition of the domain knowledge
according to the type of decisions that the KBS will support and the topics relevant
to these decisions. It requires some familiarity with the domain knowledge, and
specifically with the types of recommendations and advice that may be useful to the
end-users. The result of the model-building phase is a map of the knowledge in the
domain as a hierarchy of topics. A typical block at the bottom of the hierarchy can
be decomposed down to the basic units of knowledge, which are sets of rules and
previously-decided similar cases.
[2] Knowledge Acquisition:
Knowledge acquisition, also sometimes referred to as knowledge elicitation, is the
process of extracting knowledge about a problem domain from expert sources. All
common knowledge-acquisition techniques are well summarised in Diaper (Diaper,
1989).
[3] Increm ental Development of Full System:
In this method, rather than attempting to produce a comprehensive and detailed
specification at the outset, an initial prototype of part of the system was first pro
duced based on a simple objective. This was then extended and refined over repeated
iterations - in other words the knowledge for the other parts of ASHSD was included
in the main knowledge base. Thus the content and hence capabilities of the system
were grown progressively.
[4] Testing and User Trials:
Exhaustive testing is carried out upon completion of each knowledge base, to make
sure that everything works as intended, both at the level of an individual knowledge
base and with sequences of knowledge bases linked together. System validation starts
with a further extensive review of the completed knowledge base, e.g. by the domain Chapters. Framework of the System 70
experts. This involves running test cases through the system, and checking that the
final advice reached is correct. The four phases of the methodology are described in
some detail in the following section.
3.5.1 Model Building
Model building determines the scope and the complexity of the task. This involved pre liminary discussions with supervisors, discussions with law students and literature review.
At the end of this phase a large paper knowledge base was produced, consisting of English divorce cases (which are representative expressions of the facts, rules and case reports, col lected from the knowledge acquisition sessions). The purpose of this phase was to generate a model of the knowledge in the domain expressly in terms of the type of knowledge used by the legal practitioners. The process of designing the overall structure can be divided into two sub-goals for two different but complementary knowledge bases:
1. Legislation or statute analysis; and
2. Representation and management of previously-decided case reports.
The main aim at this stage is to identify the nature of the incidents that form the basis for the matrimonial-home-related problems in a restricted sense. In order to do so we need to have a model of the actual situation. This model contains a representation of the text of the legislation and the previously-decided case reports. The matrimonial-home-related problem classes in marital breakdown are shown in Figure 3.1. The divisions represented the nature of the incidents that form the basis for matrimonial home settlement problems.
The major categories include:
1. Owned matrimonial home settlement;
2. Tenancy transfer for rented home; and
3. Severity of injunction to restrain the offending behaviour of the spouses. Chapter 3. Framework of the System 71
LEGAL ANALYSIS
MATRIMONIAL HOME RELATED PROBLEMS IN MARITAL BREAKDOWN
OWNED MATRIMONIAL RENTED MATRIMONIAL OFFENDING BEHAVIOUR HOME HOME
OWNED HOME SETTLEMENT TENANCY TRANSFERRED SEVERITY OF INJUNCTION
Figure 3.1: Decomposition of legal analysis block
Hence, the representation of the legislation and previously-decided case reports in this areas are reflected clearly in the ASHSD knowledge-base. In order to minimise the chance of errors and misinterpretations in the knowledge base, we have to try to establish a one- to-one representation of the text of the legislation and related case reports. Apart from leading to sound models, this modelling technique also has two other advantages that we regard as important for the ASHSD project. These advantages are;
1. the clarity of the models, making it easy to discuss them with legal professionals;
and
2. the ease of translating from legal knowledge sources to an internal representation.
The ‘owned matrimonial home’ block is further decomposed and shown in Figure 3.2.
The divisions represent the nature of the incident that forms the basis for owned-matrimonial- home-settlement-problems in divorce. The major categories include: transfer the home to one of the spouses, retain the home for the caretaker of the dependent children, and divide the money between the spouses.
Similarly the other blocks - e.g. ‘rented matrimonial home’ and ‘offending behaviour’ can be expanded. These blocks can be further decomposed and expressed in internal Chapter 3. Framework of the System 72
OWNED MATRIMONIAL HOME
OWNED HOME SETTLEMENT
TRANSFER THE MATRIMONIAL SELL THE HOME AND DIVIDE KEEP THE HOME FOR THE HOME TO ONE OF THE SPOUSES THE MONEY DEPENDENT CHILDREN AND CARE-TAKER
Figure 3.2: Decomposition of owned matrimonial home block
representation form which contains a ‘chunk’ of knowledge organised as a set of rules and related previously-decided cases.
3.5.2 Knowledge Acquisition
The objective of the knowledge acquisition phase has been to find which terms, cases, laws and rules should be included in our hybrid KBS. In this phase the scope and the complexity of the task has been determined. This involved further discussions with supervisors, and with law students, and a literature review.
The legal experts do not figure prominently in our research on knowledge acquisition, implying that we give very little attention to the problem of eliciting knowledge from human experts. We have found legal experts to be expensive and not readily available within a reasonable time.
Of course, this does not imply that legal knowledge as laid down in legislation and/or case law can flow directly into KB systems without any additional transformation. It too must be analysed, structured, and represented in some ways before it can be used in a
KB system. KB systems in law should, therefore, acquire their knowledge from the same sources as lawyers do, i.e. primarily from legislation and/or case law, and secondarily from expert knowledge as discussed in chapter 4. Chapter 3. Framework of the System 73
ASHSD analyses new situations and resolves the problems in those situations by using
RBR and CBR. Analysing new cases requires a description of the facts of the new case, and a mechanism for associating the facts. There was also a need for certain concepts to be used in relation to the throughput of KB systems in law and, more specifically, the breadth and the depth of the legal knowledge required. ASHSD thus came to use two types of knowledge as described below:
1. the statute-knowledge source,
2. previously-decided case information, and
3. incomplete knowledge or fuzzy knowledge.
Legislation or Statute Knowledge Sources
The legislation is represented in the knowledge base by making use of IF < condition(s)>
T H E N
ASHSD.
Injunctions and Family Protection Orders
Usually, applications for injunction and family protection orders are made for women
(but men are equally entitled to protection and the same principles apply). The order that the courts can make falls basically into one of two categories, as shown in Figure 3.3.
These orders are :
1. Orders designed to protect the applicant and/or children personally; and
2. Orders dealing with the occupation of the family home. Chapter 3. Framework of the System 74
OFFENDING BEHAVIOUR
PERSONAL PROTECTION ORDER EXCLUSION ORDER
Figure 3.3: Decomposition of offending behaviour
The following is a part of section 16 of DPMCA^ (Gravells, 1992) which deals with the main orders available for the protection of a party to a marriage, or a child of the family.
(1) Either party to a marriage may, whether or not an application is made by that party
for an order under section 2 of this Act, apply to a magistrates’ court for an order
under this section.
(2) Where on an application for an order under this section the court is satisfied that
the respondent has used, or threatened to use, violence against the person of the
applicant or a child of the family and that it is necessary for the protection of the
applicant or a child of the family that an order should be made under this subsection,
the court may make one or both of the following orders; that is to say :
(a) an order that the respondent shall not use, or threaten to use, violence against
the person of the applicant;
(b) an order that the respondent shall not use, or threaten to use, violence against
the person of a child of the family.
(3) Where on an application for an order under this section the court is satisfied -
(a) that the respondent has used violence against the person of the applicant or a
child of the family, or
^Domestic Proceedings and Magistrates Courts Act 1978. Chapter 3. Framework of the System 75
(b) that the respondent has threatened to use violence against the person of the
applicant or a child of the family and has used violence against some other
person, or
(c) that the respondent has in contravention of an order made under subsection (2)
above threatened to use violence against the person of the applicant or a child
of the family,
and that the applicant or a child of the family is in danger of being physically injured
by the respondent (or would be in such danger if the applicant or child were to enter
the matrimonial home) the court may make one or both of the following orders, that
is to say :
(i) an order requiring the respondent to leave the matrimonial home;
(ii) an order prohibiting the respondent from entering the matrimonial home.
Whether a respondent has used or threatened violence will usually be simply a question of fact. If the respondent denies the alleged behaviour, the legal decision-maker will have to hear the evidence and make up his own mind. No guidance is given in the Act as to how the legal decision-maker should reach the decision of what is necessary for protection of the applicant and the children. Thus legal decision-makers have to use their own view as to what is necessary.
Considering the relevant section of the above legislation, one of the rules that can be deduced is shown in Figure 3.4. This rule belongs to the available-actionfsj type mentioned in chapter 1 (section 1.6.3) and is responsible for giving possible option(s) for a particular situation. This type of rule involves a straightforward transformation of statute to IF
As an example of law, a set of ASHSD’s prediction rules which can be derived from the relevant legislation, considering all other related aspects, is shown in Figure 3.6 and
Figure 3.7. The preconditions of each prediction rule are divided into three classes, namely peripheral, significant and essential. More details about the actual classification are given Chapter 3. Framework of the System 76
A R U L E 2 9
IF
(applicant is the wife) (respondent is the husband) (respondent has used violence against a child of the fam ily) (respondent has threatened to use violence again)
THEN
The court m ay m ake one or both of the following orders: [1] an order requiring the respondent to leave the m atrim onial hom e; [2] an order prohibiting the respondent from entering the m atrim onial hom e.
Figure 3.4: An ‘available-action(s)’ rule for an injunction-related case
in chapter 6 (section 6.2). Each rule was given a unique name (e.g. BRULEOl, BRULE02, etc.) beginning with ‘B’. It is worth mentioning that the prediction rules were not straight forward transformations of statute material to IF < condition(s)> T H E N
All the knowledge elicitation was carried out by the researcher. However, from time to time he was helped by undergraduate law students and the initial development involved the creation of the architecture of the system on paper and hand drafting of rules. The rules were derived mainly from the texts - statutes, cases, and text-books. Though a statutory provision may have a terse form, and consequently is a compact way of representing a certain rule in natural language, it is only a representation. Therefore, the appropriate rule has to be derived through interpreting the text. In this project all the interpretation was done by the researcher with the help of law students. The activity involved a mixture of approaches that would be quite normal by the standards of typical commercial knowledge- elicitation projects except that those projects would involve more proactive behaviour by the experts at all stages. In the present exercise, the researcher supplied the initiative because there were limits to what the experts would do without inducements (financial etc.), and we had no such inducements to give. Chapter 3. Framework of the System 77
A R U L E 0 3 IF (applicant is the wife) (respondent is the husband) (divorce has been granted) (applicant appealed for hom e settlem ent)
THEN
The court may m ake one or more of the following orders : [1] an order that a party of the m arriage shall transfer to the other party, to any child of the fam ily or to such person as m ay be specified in the order for the benefit of such a child such property as m ay he so specified, being property to w hich the first - m entioned party is entitled, either in possession or reversion;
[2] an order varying for the benefit of the parties to the m arriage and of the children of the fam ily or either or any of them any ante nuptial or post-nuptial settlem ent (including such a settlem ent m ade by will or codicil) m ade on the parties to the m arriage;
[3] an order extinguishing or reducing the interest of either of the parties to the m arriage and an order for transfer of property in favour of children w ho have attained the age of e i g h t e e n .
Figure 3.5: An 'available-action(s)' rule for an owned home settlement case
The flexibility for building offered by the Fuzzy Rule-Based shell (FuzzyCLIPS)^ meant that initially the researcher was able to select a particular area of interest - such as es tablishing tenancy transfer - and very quickly build up a basic set of rules to cover the chosen topic. However, this initial rule set was far from complete even in the context of very limited coverage of the chosen topic.
The researcher came quickly to understand what was required, and was soon producing a first draft of the rules for himself. He then handed these draft rules over to the legal expert (not a law student!) who refined and rephrased them as necessary to form a working
^ Fuzzy CLIPS is an extended version of the CLIPS rule-based shell for representing and manipulating fuzzy facts and rules, developed on behalf of the National Research Council of Canada. CLIPS was developed by the Artificial Intelligence Section, Lyndon B. Johnson Space Center, NASA, USA. Chapter 3. Framework of the System 78
B R U L E l l O
IF
Peripheral: (home type is rented) (applicant is the wife) (respondent is the husband) (length of the m arriage is long)
Significant: (divorce proceedings are pending) (respondent denies the alleged assault) (applicant is the official tenant)
E s s e n t i a l : (respondent has used violence against the applicant) (evidence of the allegation is corroborated) (severity of the allegation is dangerous) (there are no dependent children)
THEN
In this case it is possible for a general exclusion order to be granted.
Figure 3.6: Rule-based ‘prediction’ in an exclusion order for protecting a spouse
p rototype.
This prototype was then shown to the expert and, invariably, this led to him identifying gaps and misunderstandings which needed to be resolved. The researcher then made the necessary amendments. The whole process was repeated until a set of rules had been produced that satisfied both the expert and the research supervisor.
Previously Decided Case Information
ASHSD has a general representation scheme using a hypernode model which we have discussed in chapter 4 in detail. This information includes participants, relationships, causal actions, facts, and matrimonial home details. ASHSD represents this information as instances of more general categories of participants, relationships, facts, and matrimonial Chapter 3. Framework of the System 79
B R U L E 1 1 2
IF
Peripheral: (home type is rented) (applicant is the wife) (respondent is the husband) (length of the m arriage is long)
Signifîcant: (divorce proceedings are pending) (respondent denies the alleged assault) (applicant is the official tenant)
E s s e n t i a l : (respondent has used violence against a child of the family) (evidence of the allegation is corroborated) (severity of the allegation is dangerous) (there are dependent children) (dependent children are living with the applicant) THEN
In this case it is possible for a general exclusion order to be granted.
Figure 3.7: Rule-based ‘prediction’ in an exclusion order for protecting a child
home details.
Since the areas covered in this project are owned matrimonial home settlement, tenancy transfer, and severity of injunction to restrain the distressing behaviour of the spouses,
ASHSD uses the classification of the problem as an index into the case base partition.
In case representation, three levels of abstraction are used. The first level contains the main information regarding a particular case and it includes the name of the case, the source of the case report, the name of the court that ruled on the case. The other informations in the first level are the pointers for the second level of knowledge which includes further details of the indexing facilities of a case, description of participants and their relationships, list of main events, details of matrimonial home, and the pointers to the third level. The multilevel knowledge representation allows manipulation of processess at the time of inferenceing. This approach of abstraction was supported by the legal expert. Chapter 3. Framework of the System 80
Incomplete or Fuzzy Knowledge
Legal decision-makers often use descriptions such as ‘length of the marriage is long’ or
‘short’ or ‘the spouses are insolvent’ or ‘solvent’ in their judgements. But there is no easily-accessible evidence on how to quantify those linguistic terms. Therefore concepts such as long, middling, short are fuzzy. Fuzziness occurs when the boundary of a piece of information is not clear-cut. In ASHSD, we have used fuzzy set theory to handle such information. Details of fuzzy information handling are given in chapter 5.
3.5.3 Incremental Development of Full System
Once the knowledge sources had been identified and the knowledge defined, these were used to guide the implementation of a prototype hybrid system. A step-by-step approach to the model and prototype development and validation helped in monitoring and controlling progress.
An overall structure was first devised for the final system - identifying the individual knowledge bases, their respective roles, and how they would link together. This process was based on the use of simple ‘block’ diagrams. Each of the knowledge bases was then examined and the individual tasks within each knowledge base were identified and mapped out, again using a diagrammatic approach. Each knowledge base were then built up in turn. The knowledge-base maps were used to select individual tasks which were then explored in detail with the aim of completing each of these, without leaving any gaps.
As each task was considered, a set of rules was produced and added to the relevant knowledge base, expanding the content and hence capabilities of that knowledge base.
Also with the rules, the corresponding user questions had to be defined. A further time- consuming activity was the drafting of the text used by the system to explain points of matrimonial-home-related law and for giving advice. Due to the exploratory nature of this form of incremental development, the organisation of the individual knowledge bases was subject to constant revision.
As work progressed, the system structure was kept under review and a number of Chapter 3. Framework of the System 81 changes made:
• it was found to be more convenient to divide the handling of matrimonial-home-
related law into three knowledge bases, rather than the two as originally planned;
and
• it was decided that a separate knowledge base covering ‘personal protection and
exclusion order’ was necessary.
The actual process of building the main knowledge bases proceeded very quickly. By the end of final implementation, the total system had grown to 160 rules and all these rules were partitioned into three areas: rules related to owned matrimonial home settlement, rules related to tenancy transfer, and the rules related to severity of injunction.
As already indicated, models such as rule-based and case-based systems, influenced by
AI, can serve as intelligent decision support systems to the decision makers involved in the
‘matrimonial home settlement’. These systems have been adequate to model the decision processes of the experts.
User Interface
Typical intended users of the system are legal professionals and law students and may not be regular computer users. Therefore, it was decided that the system should be suitable for use by those with little or no prior knowledge of computers.
To meet the needs of these target users, the system interface needed to be easy to use and devoid of intricacy - both in computer terms and in terms of assumptions regarding prior knowledge of the law on matrimonial home problems in divorce. However, designing a simple system for those with no prior experience could so very easily produce a system that would be found to be laborious by those with experience, with the danger that they would ‘switch off’ very rapidly and be unwilling to use the system. It is important to remember that the users with prior knowledge, either about computing or the particular part of the matrimonial home settlement that ASHSD covers, are likely to became very Chapter 3. Framework of the System 82 familiar with the system very quickly. Thus, while they may start off quite satisfied with an interface designed for beginners, they may rapidly outgrow this and find the system pedestrian.
The solution to this conflict of requirements is:
1. explain the underlying terminology and concepts, leaving no uncertainty as to what
is expected by way of an answer from the user;
2. make periodic use of text explanations to tell the user what is going on;
3. use a predominantly menu form of questioning;
4. provide supplementary explanations, which can be brief or copious, at a frequency
selected by the user;
5. use of a menu style of questioning which reduces the amount of typing or retyping
required of users.
3.5.4 Testing and User Trials
Throughout the system-building process, testing of the system was carried out continu ously:
1. incremental testing by the researcher to ensure that the developing system actually
worked, and that it worked as he thought it should;
2. regular reviewing of the system by the academic supervisors, with them running the
system and examining both the rules and all the associated text - both in questions
and conclusions, and the material used to provide explanations.
Further thorough testing was carried out by the researcher on completion of each knowledge base to make sure that everything worked as intended, both at the level of individual knowledge bases and with sequences of knowledge bases linked together. Chapter 3. Framework of the System 83
System validation started with an extensive review of the completed knowledge bases by the expert. This involved running test cases through the system, using the knowledge bases in their correct sequences, to check that the final conclusions being reached were correct.
In fact, there was considerable overlap between the parts of the processes described above since the initial validation of one part of the system might identify problems requir ing change by the researcher, who then modified the knowledge base(s) concerned, before the whole cycle was repeated.
With the system fast approaching a state where both the researcher and the academic supervisors were satisfied with its performance, the researcher arranged for the system to be reviewed by a legal expert. This process is summarised in chapter 7.
3.6 The ASHSD Hybrid Reasoning Model
Figure 3.8 illustrates the diagrammatic representation of the hybrid model for the sys tem. It defines the organising schema by highlighting the various components such as the knowledge sources, control and the knowledge base, which consists of rule base and case base.
3.6.1 System Control Mechanism
The role of the control component is to co-ordinate the interaction between the various knowledge sources to discover the best possible solution for a new case. The model re produces the decision makers’ opportunistic reasoning processes through the interaction of the various knowledge sources in this architecture. The decision process is described below:
The system takes a description of a new case and produces initial advice for the user.
Once a rule from the rule base has triggered, the system then selects the most similar cases from the case base. Finally, the system displays the outlines of the relevant cases and the Chapter 3. Framework of the System
New Interface to □ = F = main computer Similar Case Seletlkm Module
k u le - B ased Mosl Similar C ases
SKLKCTÏON MODULE
A dvice o r Partial Advice Kule nrlnx Module
Figure 3.8: System layout
previous decisions in the order of their closeness with the new case. These decisions help the user to reach a final conclusion. On the other hand, when none of the rules triggers, then the system selects the rules that are nearest to triggering - using a scoring mechanism which is discussed in chapter 6 (section 6.2).
Rule-Based Advice
A rule links a set of patterns (known as preconditions) on its left-hand side (LHS) to one conclusion on its right-hand side (RHS), as shown below:
IF
When all the preconditions of a rule match the facts of a new case, then the appro priate conclusions (e.g. available-action(s)^ prediction with justification) are presented to the user. We have already mentioned in chapter 1 (section 1.6.3) that ASHSD’s available- a ction(s) rules can present two types of output: response, or no response. T he prediction rules can present three different types of prediction. On the basis of these available- action(s) and prediction, ASHSD can provide three type of rule-based advice: com pre hensive advice, partial advice, and no advice. The steps that the system uses to generate different types of advice are discussed in chapter 6.
Method of Retrieval of Similar Cases
Previously-decided cases have been stored in the case base using nested hypernode (see chapter 4) data structures. Each case was defined using the following attributes:
CAS En = {$caseJndex,$casejname,$source,$courtjname,
%participants_andjrelations, % facts, %matJiome
%causeMnd-actions}
Each case has several key events and features which act as indexes. The importance of an index hypernode is that it points towards three different subsidiary index hypernodes
(known as FIRST.INDEXid, SECOND JNDEXidi and THIRD.INDEXid) discussed in detail later on. The system can select and display the most similar cases from the case base.
Again the decisions from these previous cases can help the user to arrive at a final decision.
A large number of previously-decided cases have been stored. How one can compare and select a similar case is discussed in chapter 6 (section 6.3).
Some of the conditions under which one case can be considered relevant to another and our approach to multiple-feature retrieval by using a three-step algorithm are discussed in chapter 6 (section 6.3). Chapter 4
Knowledge Representation and Organisation
4.1 Introduction
We have discussed in chapter 2 a number of problems with the present approaches to representing legal knowledge and reasoning with it. We have also introduced in chapter 1 the rule and case representations that are common in computations with legal knowledge.
We now describe in some detail these two representation techniques, keeping in mind the knowledge modelling process that we use in ASHSD.
4.1.1 Legal Knowledge in Rule Form
The primary task of the rule-base designer is the acquisition and representation of domain- specific knowledge. Statute law has for a long time been the primary source of formal knowledge acquisition to create rules in the IF - < condition(s) > - T H E N - < conclusion
> format [e.g. (Sergot et ah, 1986b), (Sergot et ah, 1986a)].
In this project, we spent considerable time to formalise a particular part of the matrimonial- home-related decision activities that are grounded on statutes concerning family law Chapter 4. Knowledge Representation and Organisation 87
RULE-BASE IF PERIPHERAL: (applicant is the wife) (respondent is the husband) (respondent is the official tenant)
SIGNIFICANT: (divorce has been cranted) (applicant financially insolvent) (respondent rinancially solvent) (applicant has no alternative accommodation to go to)
ESSENTIAL: (privately rented property)
THEN
Rule structure
Figure 4.1: The general rule structure antd a tenancy transfer rule
(Gravells, 1992) and textbooks [e.g. (Cretney, 1992), (Black & Bridge, 1992)]; in order to transform natural-language knowledge to an IF - < condition(s)> - THEN -
If the given facts of a new case satisfy the conditions of a rule, we can draw legal conclusions by applying that rule. However, in actual cases, it is not usual for legal consequences to be obtained by rule-based information alone (Pal & Campbell, 1995); instead, we require both rule-based and case-based information. How the previously- decided cases can be represented in the case base will be discussed next. Chapter 4. Knowledge Representation and Organisation 88
4.2 Legal Knowledge in Case Form
It is necessary to represent the case law in some form that can be manipulated by programs.
There have been two major approaches to representing legal cases for CBR: representing the entire case as a single frame, and providing a formal (non-frame; specially logic-based) representation for all the facts of the case [e.g. (Ashley, 1987), (Branting, 1991)]. Each approach has its strengths and drawbacks. Also, there remains a lack of consensus as to what information should be represented in a case. One reason is the variety of functional requirements on a case representation. The result has been that case representations range from flat lists of features to rich causally-annotated descriptions of reasoning processes.
When lawyers compare cases, they compare the facts, each side’s interpretation of the facts, each side’s arguments, and disagreements about those arguments. Representing the case as a single flat record or similar structure does not support reasoning about all these underlying themes in the case because it does not represent interconnections among the facts and themes of the case. Our multi-layer representation has implications for much of the case-based reasoning process including case comparison, selection and retrieval mechanisms.
A great deal of work has been published to date in the field of case-based reasoning and the legal decision-making process. Legal decision-making systems need to represent and manipulate large amount of knowledge, e.g. previously-decided case reports. Traditionally, database management systems have performed this role. Such systems are geared towards data rather than knowledge. Today’s database and knowledge-based researchers [e.g.
(Zaniolo, 1985), (Maier & Stein, 1987), (Kim, 1990)] are giving more emphasis to deductive and object-oriented paradigms. In contrast, the birth of the graph-based data model approach (Tompa, 1989) has motivated us to use it for knowledge representation. In our work, cases are viewed as a collections of facts and these facts are represented in a graph-based scheme that relies on the hypernode model(Levene & Loizou, 1995). We have encoded the cases manually in ASHSD - keeping in mind to capture a lawyer’s notion of the ‘facts’ of a case. The main component of the hypernode graph model consists of a data structure, called the hypernode^ a directed graph whose nodes may themselves refer to further directed graphs. This hypernode model approach allows simple support of Chapter 4. Knowledge Representation and Organisation 89 modelling, representation and storage of complex knowledge sources. The main advantages of the hypernode-data model are [i] this formalism is equivalent to a hierarchy of frames, but with more formal support, [ii] this model provides inherent support for nesting of information, e.g. via graphs whose nodes can themselves be graphs, and [iii] support for data abstraction, allowing each real-world object to be represented as a separate graph
(both parts of [iii] are significant aids to knowledge representation and manipulation).
A hypernode case base is a finite set of hypernodes which are used to represent real- world objects. We can define the hypernode case base (or simply a case base in our work),
HCB, by a set of graph defining equations.
We start with by the definition of a directed graph (digraph) - a digraph is an ordered pair (A^, E), such that is a finite set of nodes containing primitive nodes and labels and
E Ç [NxN) is a finite set of directed edges or arcs which are ordered pairs of nodes from
N. For the purpose of the Hypernode Model we need two disjoint sets of constants, a countable set of primitive nodes, P, and a countable set of labels, L. In our model we assume the following domains:
1. A dom ain of labels L of hypernodes, whose elements are denoted by strings beginning
with an uppercase letter. The labels are unique and act as object identifiers.
2. A domain of primitive nodes P which are partitioned into two disjoint domains,
namely Atomic Values AY, and Attribute Names A N . AV is denoted by strings with
double quotes, and attributes by strings beginning with $ - followed by a lowercase
letter for naming purposes.
3. A domain of AV contains a distinct value which is unknown and means ‘value exists
but not known’. This is denoted by ‘unknow n’.
The digraphs of the Hypernode Model are defined by equations of the form :
G =(A ,P) Chapter 4. Knowledge Representation and Organisation 90
where G eL and {N^E) is a digraph such that N Ç (FU L). Now we take a small example to clarify the above model. For example, the adult participants (AP) and their personal details (PD) are defined by the labels API, AP2, PDl and PD2 respectively and Sparticipant, $spouse, $name, $sex, $age, “Tom”, “male”, 35, “Joe”, “female”, 27 are primitive nodes. Therefore we can define the following hypernode equations;
API = {{$participant,$spouse, PDl, AP2},{$participant — )■ APl,$spouse — >■ AP2})
AP2 = ({$participant,$spouse, PD2, API}, {$participant —!■ N 2,$spouse —)■ APl^)
P D l = [{%name,%sex,%age,'‘'Tom",“male'',^^},{%name —>■ “Tom",%sex —¥ “male",%age -¥ 35 })
PD2 = ({$ n a m e, Ssex, $a£re, “Joe", “female" ,25}, {Sname -> “Joe",%sex “ female" ,$age 25 })
A hypernode case base (or simply a case base), HCB, is a finite set of hypernodes satisfying the following conditions:
1. C O N l: No two hypernodes in HCB have the same defining label, i.e. labels are
unique.
2. C 0N 2: For any label (say G) in the node set of a digraph of a hypernode in HCB
there exists a hypernode in HCB whose defining label is G.
The hypernodes shown in Figure 4.2 comprise a portion of a hypernode case base. We note that by condition CONl each hypernode representing one of the objects in the case base has a unique label. Furthermore, the defining labels of the participant hypernodes are part-of the hypernode with the defining label ‘participants’. Thus, by condition C0N2 there must be one hypernode in the case base for each participant. Each event, participant or object in a past case has an associated unique label.
ASHSD groups all the information structures used to represent the facts and legal Chapter 4. Knowledge Representation and Organisation 91
CASE. BASE
CASEOl CASE02 CASEÜ3 CASE04 $ca5e_index ------INDEXOl $case_name ------"Bassett v Bassett" CASE05 CASE06 CASE07 CASE08 $source ------"[1975] 1 All ER 513" $court_name ------COURTOOl CASE09 CASEIO CASEll CASE12 $participants_and_relations PRELATIONSOl $fact5 ------FACTSOl CASE13 CASE14 CASE15 CASE16 $mat_home ------MATHOMEOl (appeal ------APPEALOl CASE17 CASE18 CASE„ $case_decislon ------DECISIONOl
Figure 4.2; Hypernode casebase
constructs in a case base. The overall organisation of a case base is shown in Figure 4.2, where CASEOl illustrates some of the structure that occurs in each of the cases named in the left-hand part of the figure.
T he ScaseJndex attribute refers to the unique characteristic associated with the case.
The Scasejname attribute is the unique name associated with a particular case. The
Ssource attribute represents the source of the case report. The $court-name a ttrib u te is the name of the court that ruled on the case. The $participantS-and^relations attributes contain all the information associated with the participants and the relationship among them. Finally, the $facts, $maChoTne, $appeal, and Sdecision attributes stand for the names of the legal constructs associated with the case.
4.2.1 ASHSD - Case Knowledge Representation and Organisation
The facts that ASHSD represents include the following:
• the participants involved
• relationships among the participants
• the descriptions of past cases
• the appeals and legal issues
• the decisions of past cases Chapter 4. Knowledge Representation and Organisation 92
PARTICIPANTSOl A PARTICIPANTOlOl
APARTICIPANTOIOI APARTICIPANT0I02 $name ------"Tony Smith" $age ------40 APARTICIPANT0I03 CPARTICIPANTOlOl $scx ------"male" $marital_status ------"divorced" CPARTICIPANT0102 CPARTICIPANT0102 $any_previous_inarriage "no" (dependents ------"unknown" CPARTICIPANT0103 CPARTICIPANT0104 (qualifications ------'"RSc." (occupation ------"technical profession" LPARTICIPANTOIOI LPARTICIPANT0102 (any_disability ------"no" (monthly Jncome ------"1000 pounds" LPARTICIPANT0103 CPARTICIPANTOlOl $realisable_assets_value "20000 pounds"
Figure 4.3: A portion of the ‘participants’ hypernode
1. Participants: The easiest interpretation of the word ‘participant’ is an agent which
has a share of or which takes part in an event. In our system ‘Participants’ are
those legal parties such as husband, wife, son, daughter or cohabitee, etc., who
can be held legally responsible for their actions. ASHSD represents participants as
instances of the appropriate general class, such as adult^ child^ legal or group with
associated properties. For example the properties for the class ‘adult participant’ are
name, age, sex, qualifications, etc. A case does not have to contain values of all the
properties for a specific participant. Thus, for example, the proposed data structure
for representing the participant ‘Tony Smith aged 40’ is shown in Figure 4.3.
In this representation each participant has a unique defining label. In the example,
Tony Smith’s hypernode defining label is APARTICIPANTOIOI. The first two digits
in this definition represent the case number and the last two digits represent the
participant number in the particular case.
Each hypernode is composed of attribute names expressing the properties of that
particular hypernode. For example, the hypernode APARTICIPANTOIOI (see Fig
ure 4.3) possesses the attributes $name, $age, $sex, $m arital^tatus, $any_previous_ma-
rriage, ^dependents, $qualifications, $occupation, $any.disability, $monthlyincome
and $realisable_assets_value. Whenever there is no information relating to the atomic
value of any attribute, in the case report, the value is denoted by unknown.
The other information we gain from this representation is that Tony Smith is a
graduate in science (e.g. B.Sc.) and that he is in a technical profession. He has
no disability, e.g. mental or physical. His monthly income and realisable assets are Chapter 4. Knowledge Representation and Organisation 93
DIVORCED_COUPLE0101 COHABIATING_COUPLE0101
$ex_husband- APARTICIPANT0103 (hoy_friend APARTICIPANTOIOS $ex_wife ----- APARTICIPANT0104 (giri_friend APARTICIPANT0106
STEPMOTHEROlOl STEPFATHEROlOl
(stepmother APARTICIPANTOIOS (stepfather APARTICIPANT0107 (children — APARTICIPANT0106 (children — CPARTICIPANT0108
MARRIED COUPLEOlOl PARENTOlOl
(hushand APARTICIPANT0102 (mother APARTICIPANTOIOI (wife — APARTICIPANTOIOI (father APARTICIPANT0107 ------*- CPARTICIPANTOlOl (children ------*- CPARTICIPANTOlOl ------*■ APARTICIPANTOIOS
Figure 4.4: Different types of relationship hypernodes.
£1000 and £20000 respectively.
Sometimes groups of participants perform actions or decisions together, and all mem
bers of the group are legally responsible for the results of those actions or decisions.
For example, a group of judges in the high.court or court_of_appeal generally
takes the final decision. ASHSD represents groups of participants as gparticipants.
2. Relationships:
The common usage of the word ‘relation’ is to denote an aspect or quality that
connects two or more things or parts as being or belonging or working together.
The definition of a relation adopted here reflects that participants are in relation
ships among themselves and have legal duties. The cases may contain explicit ref
erences to relationships or may contain enough information to allow inference that
a relationship exists. Relationships are such things as divorced_couple, cohab-
iting-couple, stepfather, stepm other, etc. ASHSD represents relationships as
instances of a class of relationships with associated properties. Relationships may
have different types, e.g. as shown in Figure 4.4 .
3. Performers:
Under this definition of a legal reasoning system, performers are participants, capable
of acting for themselves or for someone else. As an example, a solicitor acts as
performer for his client. A performer does not always bear full legal liability for his
actions if he is acting for someone else, but does have legal obligations to his client. Chapter 4. Knowledge Representation and Organisation 94
MATRIMONIAL_HOME
RENTED RENT_FREEOWNED
BOUGHT_OUTRIGHT_HOME INHERITED_HOME GlFTED_HOMEMORTGAGED.HOME
Figure 4.5: Different types of matrimonial home.
4. Objects:
Objects are a special class of participant. Objects are always acted upon by some
action. Any participant (e.g. adult, child, legal representative, etc.) can be an
object. In addition, any physical thing owned by a participant can also be a ob
ject. For instance, matrimonial home, own business, farm house, etc. are ASHSD
objects in representation. In this representation, matrimonial home has been par
ticularised into different object types. It is achieved by grouping objects that have
common properties. In our case these objects would be rentedJiouse, rent-free-house,
bought-outright-home, mortgaged-home, inherited-home and gifted-home. In other
words, classification is a form of abstraction in which a composite object, in this
context called object type or class, is defined as a set of simple objects, which have
the same properties and are called instances. ASHSD categorises objects for type
of matrimonial homes. The different types of matrimonial homes are shown in Fig
ure 4.5 and the corresponding hypernodes are shown in Figure 4.6. Categorisation
helps to organise the objects in such a way that retrieving the information is easier.
Our definition of an object, as illustrated above, excludes the way an object is used
in object-oriented techniques or methods.
5. Owners:
A participant may own a non-human object or objects. ASHSD represents ownership
as instances of a particular class of ownership such cLs owner of a car, owner of a
house, owner of a pet etc.
6. Durations: Chapter 4. Knowledge Representation and Organisation 95
RENTED HOME MORTGAGED HOME
$landlord ------"private land lord" (owner ------APARTICIPANTOIOI $olTicial_tenant ----- APARTICIPANTOIOI (conveyance_date ------"10 January 1985" $date_of_occupation "10 March 1970" (purchase_price ------60000 $duration_of_stay — "25 years" (present_price ------90000 $monthly_rent ------500 (busbands_contribution ---- 10000 $current_occupants - APARTICIPANT0I02 (wifes_contribution ------0 $tenancy_condition - "with agreement" (mortgage_amount ------40000 $no_of_bedrooms — 2 (mortgage_outstanding ----- 8000 (mortgage_matu ration_date "10 January 1997" (current_occupants ------APARTICIPANT0102 RENT FREE_HOME (propertyjype ------"freehold" (no_of_bed rooms ------4 $legal_owner APARTICIPANT0I05 (tenant ----- APARTICIPANT0I02 $date_of_occupation "15 April 1980" INHERITED_HOME $duratlon_of_stay— "15 yesrs" $current_occupants - APARTICIPANTOIOI (owner — APARTICIPANT0105 $property_type ----- "freehold" (conveyance_date — "25 April 1987" $no_of_bed rooms — 3 (d u ratlon_of_s tay —- "8 years" (current_occupants ---- —► APART1CIPANT0102 (restrlctlon_to_sell — ► "no" (no_of_bed rooms ------— 2 BOUGHT OUTRIGHT HOME
(owner ------APARTICIPANT0102 (conveyance.date "10 January 1975" GIFTED.HOME (purchase_price - "70,000 pounds" (present_price "100000 pounds" (owner ------APARTICIPANT0102 (husband’s_contribution 20000 (conveyance.date ------"20 May 1983" (wife’s_contribution — 5000 (d u ration_of_s tay ------► "5 years" (duration_of_stay ------"20 years" (current_occupants ------APARTICIPANTOIOI (current_occupants ------APARTICIPANTOIOI (resttictlon_to_sell ------"no" (no_of_bedrooms ------2 (no_of_bed rooms ------3
Figure 4.6: Different types of matrimonial home hypernodes.
Duration describes the time period during which an event has happened. ASHSD
represents duration as years, months, etc. ASHSD represents actual time using the
special predicate ‘date’ which has the properties ‘day’ , ‘month’ and ‘year’ associated
with it.
4.3 Example: Martin (B.H.) v Martin (D.)
To illustrate the representation scheme, an example of a represented past case follows. The case is Martin v, Martin, and the example includes the judicial opinion, facts and legal constructs of the case hand-coded from the judicial opinion. ASHSD’s case base consists of the representation for at least 50 cases hand-coded from different British legal sources. Chapter 4. Knowledge Representation and Organisation 96
CASE17
INDEX17 "Martin v Martin" "[1974] Earn 12" COURTOOl $participants_and_relations —*■ PRELATIONS17 FACTS17 MATHOME17 APPEAL17 DECIS10N17
Figure 4.7: The main case hypernode for the Martin v Martin case
4.3.1 Judicial Opinion for Martin v Martin
[COURT OF APPEAL] M artin (B .H .) v M artin (D .) 1978 Earn 12
1977 March 10 Stamp and Ormrod L.JJ. and Sir John Pennycuick
Husband and Wife - Matrimonial home - Divorce - Trust for sale - Wife living in matrimonial home - Husband living with tenant of council accommodation - W ife’s share in equity insufficient to purchase alternative accommodation - Whether order for sale to be made - Court’s exercise of discretion - Matrimonial Causes Act 1973 (c. 18), ss. 24(1),
After a childless marriage lasting 15 years the husband left the matrimonial home which he had bought during the first year of the marriage and went to live with another woman who had a tenancy of a council house. The wife remained in the matrimonial home, obtained a divorce and applied for a property adjustment order of the husband’s interest in the house. The equity in the house was worth approximately £10,000.
The registrar ordered a sale and a division of the net proceeds between the parties in equal shares. Purchas J. reversed the registrar’s order and ordered that the house should be held upon trust for the wife during her life or until her remarriage or such earlier date as she should cease to live there and thereafter upon trust for the parties in equal shares.
On appeal by the husband Chapter 4. Knowledge Representation and Organisation 97
INDEX17 SECONDJNDEXn
-----(home type is freehold) FIRST_INDEX17 -----(applicant is the wife)
-----*- (respondent is the wife) SECONDJNDEX17 -----► (respondent is the owner of the home)
THIRD INDEX17 ------(respondent left home)
-----► (respondent is cohabiting with the new partner)
-----*• (allegation of adultery against the respondent) -----► (divorce has been granted) -----► (respondent wants to sell the home) -----»■ (home is on joint trust for sale) ___ (home sale is postponed)
FIRST INDEX 17 THIRD INDEX17
$no_of_dependent_children ------0
(index "owned home settlement" $length_of_stay_in_the_mat_home 20
$no_of_step_children ------3
$no_of_dlvorce_offsprlng 0
$Iength_of_marriage ----- long
Figure 4.8: Index hypernodes for the Martin v Martin case
PRELATI0NS17
PARTICIPANTS 17
RELATIONS17
Figure 4.9: Participants and relation hypernodes for the Martin v Martin case
Held, dismissing the appeal, that when the only available asset was the matrimonial home the most important circumstance to be taken into account in applying section 25 of the Matrimonial Causes Act 1973 was that both parties should have a roof over their heads, and that applied equally whether or not there were children of the marriage; that, while the husband was living in a council house with a woman whom he intended to marry and had no immediate need of capital to support his present way of life, the wife needed either the matrimonial home or some alternative accommodation and, since the registrar had found that her share in the equity of the matrimonial home would be insufficient to enable her to purchase alternative accommodation and an order postponing the sale of the Chapter 4. Knowledge Representation and Organisation 98
PARTICIPANT!?
APARTICIPANT17 LPARTICIPANT17
APARTICIPANT1701 LPARTICIPANT1701 APARTICIPANT1702 LPARTICIPANT1702 APARTICIPANT1703 LPARTICIPANT1703 LPARTICIPANT1704 LPARTICIPANT1705 LPARTICIPANTI706 LPARTICIPANT1707 CPARTICIPANTI7
CPARTICIPANT170I CPARTICIPANTI702 GPARTICIPANT17 CPARTICIPANT1703 GPARTICIPANT1701
Figure 4.10: Participant hypernodes for the Martin v Martin case
matrimonial home until some specified date in the future might cause great hardship to the wife when the time arrived, the correct course was to give the wife the right to occupy it for as long as she needed it.
P er curiam . A question of public policy is involved if, in reliance on a local authority providing housing, an order is made for the sale of the matrimonial home so that the parties’ capital may be released.
P er Ormrod L.J. In exercising the wide discretionary power given by the Matrimonial
Causes Act 1973 the court should preserve the utmost elasticity to deal with each case on its own facts. Decisions of the Court of Appeal can never be better than guidelines. They are not precedents in the strict sense of the word. There is bound to be an element of uncertainty in the use of the discretionary powers given to the court under the Act, and no doubt there always will be, because as social circumstances change so the court will have to adapt the ways in which it exercises discretion.
Decision of Purchas J. affirmed. Chapter 4. Knowledge Representation and Organisation 99
4.3.2 Knowledge Representation and Organisation for Martin v Martin case
The case hypernode for M artin v M artin, shown in Figure 4.7, contains pointers to all of the legal constructs and facts used to represent it.
Index hypernode of M artin v M artin case
The index hypernode refers to the unique characteristics associated with the particluar case. These unique characteristics refer to the very important facts or events involved in each case. Figure 4.8 shows the representation of the caseindex hypernode for the M artin
V M artin case. The INDEX17 hypernode points towards the three hypernodes F lN D E X ir,
SINDEX17 and TIN DEX17. T he FIND EX17 hypernode’s atomic value is owned home settlement which means that the present case is related to settlement of an owned matrimonial home.
This settlement may be for sale and equal distribution of the equity or it may be for transfer of the ownership of the home to the non-owing spouse while postponing its sale for a definite time.
The SINDEX17 points to the characteristic features ‘home type is freehold’, ‘applicant is the wife’, ‘respondent is the husband’, ‘respondent is the owner of the home’, ’respondent left home’, ‘respondent is staying in a separate accommodation’, ‘respondent is cohabiting with the new partner’, ‘allegation of adultery against the respondent’, ‘divorce has been granted’, ‘respondent wants to sell the home’, ‘home is on joint trust for sale’ and ‘sale postpone o f home ’. T he TIN DEX17 represents the more detailed information relating to the case in hand. The atomic value of the attribute $no-of_dependent-children is 0, which means that the couple have no dependent children at the time of the dispute. Similarly, other attributes, for example, Slength-ofstayJnJhe-mat-home, $no-of^step_children,$no- of^divorce.offspring, and $length.of_marriage can be defined in term of 20 years, 3, 0, and long. The concept of fuzzy linguistic variables (e.g. long, very long, somewhat long, etc.) is used; we summarise our use of fuzzy qualifiers in chapter 5 (section 5.3 ). Chapter 4. Knowledge Representation and Organisation 1 0 0
APARTICIPANT1701 APART1CIPANT1702
$name ------"unknown" (name ------"Daphne Martin" $age ------43 (age ------46 $sex ------'m ale' (sex ------"female" $marital_status------"married " (marital_status ------"married" $any_previous_marriage "no" (any_previous_marriage "no" $dependents ------""no" (dependents ------"no" Squalifications ------"unknown" (qualifications ------"unknown" (occupation ------"unknown" (occupation ------"unknown" $any_disability ------"no" (any_disability ------"no" $monthly_income ------120 pounds (monthlyjncome ------92 pounds $reallsable_assets_value "no significant amount"' (realisable_assets_value "no significant amount"
APARTICIPANT1703 CPARTICIPANT1701
(name ------"unknown" (name ------"unknown" (age ------"unknown" (age ------*■ 12 (sex ------female" (sex ------*- "unknown" (marital_status ------"divorced"" (any_disabi!ity —► "no" (any_previous_marriage "yes" CPARTICIPANT1701 CPARTICIPANT1702 (dependents — CPARTICIPANT1702 CPARTICIPANT1703 (name ------"unknown" (qualifications ------"unknown" (age ------9 (occupation ------"uknown" (sex ------"unknown" (any_disability ------"no" (any_disability (monthlyjncome ------"unknown" (realisable_assets_value "unknown" CPARTICIPANT1703
(name ------"unknown" (age ------"unknown" (sex ------"unknown" (any_disability "unknown"
Figure 4.11: Adult and children participant hypernodes for the Martin v Martin case
Participants-and-relations hypernode of the M artin v M artin case
The participants_and_relations hypernode contains all the information associated with participants and the relationship among them. Figure 4.9, 4.10, 4.11, 4.12 and 4.13 show the participants and their relationships.
Facts hypernode of the M artin v M artin case
The fact hypernode represents the facts of a particular case. Figure 4.14 shows the rep resentation for the fact hypernode for M artin v M artin. The couple were married in
January 1957 and there were no children. The husband is now 43 and the wife 46. On
September 27, 1972, the husband left the matrimonial home to live with another woman Chapter 4. Knowledge Representation and Organisation 101
LPARTICIPANT1701 LPARTICIPANT1704
(name — "Mr. Tickle" (name —► "L. J. Ormrod" (profession — "Registrar" (profession —► "Judge" (work_for COURTOlO (work_for COURTOOl
LPARTICIPANT1702 LPARTICIPANT1705
(name — "J. Purchas" (name —► "Sir John Pennycuick" (profession "Judge" (profession "Judge" $work_for COURTOOl (work_for COURTOOl
f 'i LPARTIC1PANT1703 GPARTICIPANT1701
(name — "L.J.Stamp" LPARTICIPANT1703 (profession —► "Judge" (gparticipants - — LPARTICIPANT1704 (work_for — COURTOOl ^ LPARTICIPANT1705
Figure 4.12: Legal and group participants for the Martin v Martin case
MARRIED_COUPLE1701 COHABITATING_COUPLE1701
$husband APARTICIPANT1701 $boy_friend APARTICIPANT1701 (wife ----- APARTICIPANT1702 $girl_friend APARTICIPANT1703
r PARENT1701 DIVORCED_COUPLE1701
(mother ■ APARTICIPANT1703 (ex_hushand "unknown" (father "unknown" (ex_wife ♦ APARTICIPANT1703 APARTICIPANT1701 (children APARTICIPANT1702 APARTICIPANT1703
Figure 4.13: Relations among the participants for the Martin v Martin case
who had 3 children from an earlier marriage. A decree nisi was granted on the basis of the husband’s adultery, and that decree was made absolute on November 19, 1974.
The present appeal is for a property adjustment order of the husband’s interest in the matrimonial home, 27, Plane Avenue, Northheet, Kent.
TEXT is a special type of hypernode which contains the actual text of a case report. In this particular case representation we have used the instances of the TEXT hypernode which are T E X T iro i in Figure 4.14, TEXT1702 in Figure 4.17 and TEXT 1703 in Figure 4.18 respec tively. Hypernode type c o u r t has been used to represent a particular court description.
For example, c o u R T o o i represents the court of appeal and is described by the attributes Chapter 4. Knowledge Representation and Organisation 102
FACTS17 TEXT1701
The couple were married in January 1957 and there were no children TEXT1701 of the marriage. The husband is now 43 and the wife 46. On September 27,1972, the husband left the matrimonial home to live with another woman who had 3 children of a former marriage. A decree nisi was granted on the basis of the husband’s adultery, and that decree was made absolute on November 19,1974. After the decree, the husband appealed for a property adjustment order and in particular his interest in the matrimonial home, 27, Plane Avenue, Northfleet, Kent
Figure 4.14: Facts hypernode for the Martin v Martin caae
COURTOlO COURTOOl
$court_name "unknown" $court_name -----»• "Court of appeal" $address ----- "unknown" (address ----- "London"
Figure 4.15: Different court hypernodes for the Martin v Martin case
MATHOME17 MORTGAGED HOME17
(owner ------APARTICIPANT1701 MORTGAGED_HOME17 $conveyance_date "January 1957" $purchase_price - 1800 $present_price — 10000 $husbands_contribution 500 $wifes_contribution ---- 0 $mortgaged_amount ---- 1300 $mortgage_outstanding - 890 $mortgage_maturation_dale "unknown" $current_occupants ------APARTICIPANT1702 $property_type ------"freehold" $no_of_bedrooms ------"unknown"
Figure 4.16: Matrimonial-home hypernode for the Martin v Martin case
Scourt^name^ and $address. T he c o u r t instance COURTooi is shown in Figure 4.15.
4.3.3 Matrimonial-Home Hypernode Structure and Contents
The type of matrimonial home of the present case is an instance o f‘mortgaged home’. The information is represented in Figure 4.16. This figure is also a further general illustration of the kind of (hypernode) structure present throughout the case side of ASHSD. Chapter 4. Knowledge Representation and Organisation 103
A P P E A L 1 7 T E X T 1 7 0 2
This is an appeal from an order of Purchas J. made on November 26, T E X T 1 7 0 2 1976, whereby he reversed an order made by Mr Registrar Tickle in relateion to the former matrimonial home of the parties to the suit The registrar had ordered a sale of the matrimonial home and a division of the net proceeds between the parties in equal shares on the footing that the husband should discharge the rather small mortgage debt which was charged on the property. The judge reversed that order, deciding that the matrimonial home should be held in effect upon trust for the wife during her life or until her remarriage or such earlier date as she should cease to live there.
Figure 4.17: Appeal hypernode for the Martin v Martin case
D E C I S I O N 1 7 T E X T 1 7 0 3
On appeal by the husband T E X T 1 7 0 3 Held, dismissing the appeal, that when the only available asset was the matrimonial home the most important circumstance to be taken into account in applying section 25 of the MCA 1973 was that both parties should have a roof over their head, and that applied eqnally whether or not there were children of the marriage; that, whilst the husband was living in a council house with a woman whom he intended to marry and had no immediate need of capital to support his present way of life, the wife needed either the matrimonial home or some alternative accommodation and, since the registrar had found that her share in the equity of the matrimonial home would be insufficient to enable her to purchase alternative accommodation and an order postponing the sale of the matrimonial home until some specified date in the future might cause great hardship to the wife when the time arrived, the correct course was to give the wife the right to occupy it for as long as she needed it
Figure 4.18: Decision hypernode for the Martin v Martin case
4.3.4 Appeal Hypernode of the Martin v Martin case
ASHSD represents an appeal hypernode as an instance of a TEXT hypernode. This hy pernode contains the details of an appeal of M artin v M artin case and it is shown in
Figure 4.17.
4.3.5 Decision Hypernode of the Martin v Martin case
The decision hypernode (DECISION17 ) represents the final court ruling that have taken place in a particular case. Figure 4.18 shows the representation for the decision hypernode for Chapter 4. Knowledge Representation and Organisation 104 the M artin v M artin case.
4.3.6 Summary
The translation of case reports into a hypernode representation was carried out by the researcher, though with the critical assistance of undergraduate law students. The entire data structure for a case report was then examined and eventually approved by the do main expert. This whole process clearly had some subjective character, because of the judgement exercised by the various participants, but the actual process did not appear to be more subjective than in ordinary legal reasoning about such cases. The ‘quality of the subjectivism’ was not as high as one would request in, say, knowledge engineering for a commercial project or a project to produce research-level results about knowledge extrac tion. In those projects, one would expect intensive and continuing participation from at least one human expert on the relevant area of knowledge. Here, however, such experts either had no time for that degree of participation or were to expensive. The adequacy of the approach used was measured by inspection by legal specialists, as mentioned above, and including use of the questionnaire in Appendix C.
A detailed representation of knowledge and organisation relevant to matrimonial home settlement in English divorce cases has been presented in this chapter. ASHSD holds the domain knowledge in two forms: IF
Reasoning With Imprecise Knowledge
5.1 Knowledge-Based System and Puzziness
In the real world much im precise or fu zzy knowledge exists. Fuzziness is associated with the difficulty of making a clear or precise qualification of a situation in some domain of interest. Fuzziness is a problem because it may prevent the decision-maker from taking the right decision and may even cause an erroneous decision. In the legal area, imprecise information may prevent the proper decision for a client or contribute to a vague judge ment. Human thinking and reasoning very often encounter fuzzy information which has been formed due to inexact human concepts. However, humans can still give reasonable answers, using the fuzzy information. Automated reasoning systems should also be able to tackle unreliable and incomplete information to imitate human-like reasoning. In many domains of application, knowledge-based systems are designed to support fuzzy reasoning, such as FRIL (Williams et al., 1989), FLOPS (Buckley & Siler, 1987), FRIL (Baldwin,
1987), FLISP (Sosnowski, 1990) and at least one classification system for credit card trans actions (Reategui & Campbell, 1994). All those systems are purpose-built for a specific application area.
105 Chapter 5. Reasoning With Imprecise Knowledge 106
5.1.1 Overview of Fuzzy Set Theory
Fuzzy systems have been around since the 1920s, when they were first proposed by
Lukasiewicz (Rescher, 1969) in the form of many-valued logic. Lukasiewicz studied the mathematical representation of fuzzy term s such as tall, old, or hot . He developed a system of logic that extended the range of truth values to all real numbers in the range of
0 to 1. He used a number in this set to represent the possibility that a given statement was true or false. For example, the possibility that a person’s 120 kilogram weight is really heavy might be set to a value of 0.99; it is extremely likely that the person is heavy. This research led to a formal inexact reasoning technique aptly named possibility theory.
In 1965, Loth Zadeh [e.g. (Zadeh, 1965), (Zadeh, 1983)] extended the work on the possibility theory into a formal system of mathematical logic. However, more importantly, he brought to the attention of the research community a collection of valuable concepts for working with fuzzy natural-language terms. This new logic tool for representing and manipulating fuzzy terms is called fuzzy logic or fuzzy set theory. The theory of fuzzy sets, hrst outlined by Zadeh (Zadeh, 1965), was developed to model the notion of concepts with ill-dehned or fuzzy membership boundaries.
5.1.2 W hat is Fuzziness ?
Fuzziness occurs when the boundary of a piece of information is not well dehned. For example, concepts such as short, long, medium, good, bad, high or low are fuzzy. There is no precise single quantitative value which defines the term good. For some people, flats near Downing Street are good, and for others, flats in the Barbican area are good. In fact the concept ‘good’ has no clear boundary. The penthouse flat near Downing Street may definitely be better than a flat near Sheffield city centre. However, a flat near the
Barbican has only some possibility of being good and usually depends on the context in which it is being considered. Unlike classical set theory where one deals with objects whose membership in a set can be described clearly, in fuzzy set theory membership of an element to a set can be partial, i.e. an element belongs to a set with a certain grade
(possibility) of membership. More formally a fu zzy set A in a universe of discourse U is Chapter 5. Reasoning With Imprecise Knowledge 107
Purchase value Grade of Membership
£250,000 1.0
£230,000 0.9
£210,000 0.8
£190,000 0.7
£170,000 0.6
£150,000 0.5
£130,000 0.4
£110,000 0.3
£90,000 0.2
£70,000 0.1
£50,000 0.0
Table 5.1: Fuzzy term ‘good’ characterised by a membership function
Pa {x ) : X [0,1]
which associates with each elementx oi X a, num ber p a {^) in the interval [0,1] which represents the grade of membership of x (e.g. a flat priced at x pounds) in the fuzzy set
A. For example, the fuzzy term ‘good’ might be defined by the fuzzy set as shown in
Table 5.1. Rather than an exact boundary, there is a gradual transition from good flats to not-good flats.
/^5ood(250,000) = 1, jUgood(230,000) = 0 . 9 , . . .pgood{^^-> 0 0 0 ) = 0
Grade-of-membership values constitute a possibility distribution of the term ‘good’.
Fuzzy set theory provides a more reasonable interpretation of good flats in central London.
A fuzzy set assigns membership values between 0 and 1 that reflect more naturally a member’s association with the set. For example, if a one-bedroom flat’s value is £250000, we might assign a membership value of 1, or if that value is £190,000, a value of 0.7. In Chapter 5. Reasoning With Imprecise Knowledge 108 this example,purchasc-value is the linguistic variable and good one of its fuzzy sets. Other sets that we might consider are bad, long, middling, etc. Each of these sets represents an adjective defined on the linguistic variable.
5.2 Fuzzy Sets - Notation, Terminology and Basic Proper ties:
5.2.1 Classical Set or Crisp Set
A classical set or crisp set divides the objects in the universe of discourse into ‘objects in the set ’ and ‘objects not in the set
The symbols U, X,V,W, with or without subscripts, are generally used to denote specific universes of discourse, which may be arbitrary collections of objects, concepts or mathematical constructs. For example, U may denote the set of all divorced couples in
London; the set of all matrimonial homes under dispute, the set of all real numbers; the set of all research students in the Department of Computer Science at University College
London, etc. For example, the set of one-bedroom studio flats in central London is a crisp set; a particular flat is either a one-bedroom studio flat or it is not.
5.2.2 Characteristic Function of a Crisp Set
T he characteristic function of a crisp set assigns a value of either 1 or 0 to each individual in the universal set (e.g. the universe of discourse), thereby discriminating between members and nonmembers of the crisp set under consideration. The membership in a crisp set A of X is represented by a function, /a, known as a characteristic function of the subset A, shown below:
1 iff x€a, fa{x) = < 0 iff x / a . Chapter 5. Reasoning With Imprecise Knowledge 109
Membership value
Figure 5.1: Characteristic function of A
1.0 ._
Membership
value 0.5- I K = 1000 pounds
50K 100k 150k 200k 250k
Purchase value of flats (in pounds) ------*-
Figure 5.2; A characteristic function for fuzzy set GOOD
The graphical representation of the characteristic function of the subset A is shown in
Figure 5.1. To extend the concept of crisp sets to fuzzy sets, the definition of characteristic function may be generalised in the following manner.
5.2.3 Fuzzy Set and its Characteristic Function
The concept of a fuzzy set represents a generalization of crisp sets. As defined above, the characteristic function of a crisp set assigns a value of either 1 or 0 to each individual in the universal set, thereby discriminating between members and nonmembers of the crisp set under consideration. This function can be generalised such that the values assigned to the elements of the universal set fall within a specified range and indicate the membership grade of these elements in the set in question. Larger values denote higher degrees of set Chapter 5. Reasoning With Imprecise Knowledge 110 membership. Such a function is called a characteristic function or memhership function and the set that it defines is a fu zzy set.
T he characteristic function or m em bership function can be defined as :
P a (^) : —>• [0,1]
In fuzzy set theory, event or element x is assigned a membership value by a membership function p. This value represents the degree to which element x belongs to fuzzy set A.
Pa {x) = D egree{x G A)
The membership value of x is bounded by the following relationship:
0 < Pa {x ) < 1
It should be noted that the value of pa {^) does not represent the probability that x is in the set A, but rather its degree of membership in A. For example, when considering the fuzzy set of good flats in central London,
/^5oo(i(190, 0 0 0 /7ai) = 0.7 .
Here, we are not stating the probability that a £190,000 flat is good; insted, this represents the degree to which the flat is a member in the set of good flats. The choice of an appropriate characteristic function is very important in developing a correct fuzzy model. The characteristic function for the fuzzy set good is shown in Figue 5.2. Chapter 5. Reasoning With Imprecise Knowledge 111
5.2.4 Formal Fuzzy Set Representation
The grade of membership p a {^) of x in fuzzy set A is a positive number and the pair
is known as a singleton. Often these pairs are represented by /i^(a;)/a: or p{x)/x for short. Let us assume that we have a discrete set of X elements ^ 1 , ^ 2 , ...... , Xn-
The fuzzy set A defines the membership function Pa {^) that maps the elements X{ of X to the degree of membership in [0,1]. The membership values indicate to what degree X{ belongs to A. For a discrete set of elements, a convenient way of representing a fuzzy set is through the use of a vector:
— (®1 > j ^n)
where
ûj — PA{xi)
For a clearer representation, the vector often includes the symbol ‘/' which associates the membership value ai w ith its X{ coordinate:
A = (ai/xi,a2/x2,...... , 0 */%*)
Standard fuzzy set notation represents the union of the vector’s dimensions as follows; where ‘+ ' represents the Boolean notation for union:
A = flilxi + P2 /X2 + ...... + Pn/Xn
If X is a continuous function, then the set A can be represented as:
^ = / PA(xi)/xi Jx Chapter 5. Reasoning With Imprecise Knowledge 112
For a continuous set of elements, we need some function to map the elements to their membership values. Typical functions used are sigmoid^ gaussian, and pi. These types of functions are smooth and can typically provide a close representation of the data that are the basis of the fuzzy set. However, these functions add to the computational load of the computer. In practice, most applications rely on a piecewise linear fit function to represent the fuzzy set.
Thus a fuzzy set A is represented as a list of singletons
< sin g leto n s > ::= (a?i pi)(x2 P 2 ) ...... (z» /in),
where
Xi < Xi+i for 2 = 1,2,..., n — 1,
Xi is an element from the universe of discourse, and
Pi is a number denoting the grade of membership of x in the fuzzy set A
Thus, a fuzzy set can be represented by an ordered set of points joined by straight-line segments. The grade of membership of an x value not listed in a list of singletons can be calculated on the basis of interpolation according to the following formula:
p(xi),x < Xi
Xi < x < Xi+i
l^(^n), ^
Using the above formula, one can define the fuzzy set ‘good flats’ in central London as follows :
Pgood(250000) = 1.0, pg,od(230000) = 0.9, Pgood(210000) = 0 . 8 ,
Pgood(190000) = 0 . 7 , pgood(™000) = 0 . 6 , Pgood(l50000) = 0 . 5 ,
/z^ood(130000) = 0.5, //gooXllOOOO) = 0 .0 , /ig ^ o j(9 0 0 0 0 ) = 0 . 2 ,
/i^ood(70000) = 0.1, /igooj(50000) = 0.0 Chapter 5. Reasoning With Imprecise Knowledge 113
The list of singletons representing the above fuzzy set is:
((250000 1.0)(230000 0.9)(210000 0.8)(190000 0.7)(170000 0.6)
(150000 0.5)(130000 0.4)(110000 0.3)(90000 0.2)(70000 0.1)(50000 0.0))
5.2.5 Hedges of Fuzzy Sets
Fuzzy set definitions enable the user to build a vocabulary of terms that describe sets with vague boundaries. These concepts can be used in a knowledge base(KB) to represent the vague terms, and the fuzzy terms are referred to as linguistic variables. The user provides a formal representation of the fuzzy terms to the KB. The system should be capable of finding the corresponding attribute-values by using this representation, after getting the appropriate high-level concepts from the user.
In normal conversations, humans may add extra vagueness to a given statement by using adverbs such as very, slightly, or somewhat. For instance, an adverb modifying an adjective, such as the flats near Barbican Centre are somewhat good, where the adverb
‘som ew hat’ is known as a fuzzy hedge.
The possibility distribution of a fuzzy concept like somewhat middling length of mar riage or very long length of marriage can be obtain by applying arithmetic operations to the fuzzy set of the basic fuzzy term. For example. Figure 5.3 shows the three fuzzy sets on length of marriage, along with the sets adjusted by the introduction of the term very derived through an operation as discussed below:
Concentration (very):
The concentration operation has the effect of further reducing the membership values of those elements that have smaller membership values. This operation is given as
/^con(A)(^) — (^ a (^)) Chapter 5. Reasoning With Imprecise Knowledge 114
Lcngth_of_niarriage
short middling long
1.0
Membership value 0.5 very very very short middling long
2 4 6 8 10 12 14 16 Length_of_marriage in years
Figure 5.3: Fuzzy sets on length of marriage with ‘very’ hedge
Given a fuzzy set of long length_of_marriage, we could use this operation to create the set of very long length-of.marriage.
Dilation (somewhat):
The dilation operation dilates the fuzzy elements by increasing the membership value of those elements with small membership values more than those elements with high membership values. This operation is given as
0.5
Given a fuzzy set of middling length.of.marriage., we could use this operation to create the set of somewhat middling length.of^marriage.
Now we discuss how ASHSD handles fuzzy knowledge, in the next section. Chapter 5. Reasoning With Imprecise Knowledge 115
5.3 Fuzzy Knowledge Representation in ASHSD
In ASHSD we use the concepts of linguistic variables and the theory of fuzzy sets to deal with legal knowledge that is expressed in imprecise natural-language terms. Our motivation comes from the importance of allowing the flexible expression of information
(i.e. fuzzy or imprecise information) in the problem and case description and managing it in the RBR and CBR parts of the ASHSD system.
In matrimonial home settlement problems, often the decision-maker has to take con sideration of the length of marriage (e.g. short, middling, long). But in legislation there is no single quantitative value for length of marriage which deflnes whether it is short, mid dling or long. However, a marriage of 14 to 15 years has been described as ‘moderately long’ in the previously-decided Dew v Dew^ case report and a ‘short marriage’ is usually taken to mean one of only a very few years, generally up to two years (Crowdy, 1992). In our research we have used a simple empirical study to determine the length of marriage intervals. We have asked a group of family law specialists^ for their understanding of length of marriage. We have asked each of these individuals to describe what he or she believes is a ‘long’ length of marriage in terms of a range of years. After acquiring answers, we performed a simple averaging calculation to produce a fuzzy set for the long length of marriage. We continued this polling to account for other lengths of marriages such as short and middling. Keeping these pieces of legal knowledge in mind, we then deflned the linguistic variables for duration of a marriage as follows.
(long (1 0)(2 0.06)(3 0.13)(4 0.22)(5 0.33)(6 0.46)(7 0.78)(8 0.89)(9 1))
(short (1 0)(2 0.99)(3 0.90)(4 0.50)(5 0.35)(6 0)(7 0)(8 0)(9 0))
(middling (1 0)(2 0.14)(3 0.39)(4 0.75)(5 0.70)(6 0.58)(7 0.44)(8 0.29)(9 0.11))
^Dew V Dew [1986] 2 FLR 341
^Legal researchers, legal practitioners, and law students. Chapters. Reasoning With Imprecise Knowledge 116
In the same way as for the length of marriage, we have determined the financial solvency of an individual. We have taken the difference between the average nett income and the average expenditure of a particular individual on a yearly basis. Referring to the surplus amount (i.e. surplus amount = average nett income - average expenditure), we have asked the same group of specialists for their opinions about the solvency of an individual.
These concepts have been used in ASHSD’s knowledge base for the definition of the lin guistic variables for solvency. The system is capable of finding the corresponding attribute- values by using this representation after getting the appropriate high-level concepts from the user. In normal conversations, humans may add extra vagueness to a given statement by using adverbs such as somewhat., very., or slightly. The possibility distribution of a fuzzy concept like somewhat middling length of marriage can be obtained by applying arithmetic operations to the fuzzy set of basic fuzzy terms. The fuzzy arithmetic opera tions are known as modifiers (Zadeh, 1981). We have used just two modifiers in ASHSD to handle the fuzzy information, which are: som ew hat and very. The legal knowledge that we have processed does not seem to demand any larger collection of modifiers.
To arrive at the proper linguistic terms for the length_of_marriage and the state of solvency of a couple, ASHSD’s inbuilt algorithm must go through several steps, which are described as below:
1. After getting numerical values (e.g. actual length of marriage in years, net surplus
amount, etc.), from the user, ASHSD’s inbuilt procedures first determine the list of
singletons by using its characteristic functions.
2. It then measures the closeness of the current list of singletons to a pre-dehned lists
of singletons for the different linguistic variables and their modifiers.
3. Finally, it finds which of the outcomes matches best with the linguistic terms for the
present case in hand.
Different methods (Schmucker, 1984) are available to measure the closeness of matching and translating to natural-language expressions from the high-level user input. One of the most straightforward methods, known as best-fit method, is what we have used in ASHSD. Chapter 5. Reasoning With Imprecise Knowledge 117
BRULEOl
IF
Peripheral: (home type is freehold) (application is the wife) (respondent is the husband) (applicant left home) (applicant wants to sell the home)
Significant: (applicant financially solvent) (respondent financially insolvent) (divorce has been granted) (there is no dependent children)
Essential: (applicant is staying in a separate accommodation) (equity is not sufficient to buy new accommodation for respondent) (possibility of getting council accommodation for respondent is very low) (equity is not enough for respondent to rent a house on a long term basis) (length of the marriage is long) (applicant is the owner of the home) (respondent is staying in the home)
THEN
The proposed advice is that the husband can stay in the matrimonial home for another year, after which the home has to be sold and the husband must find some other suitable accommodation for himself
Figure 5.4: The advice and the preconditions of BRULEOl
In this method the selection of a particular linguistic term is achieved by calculating the
Euclidean distance^ from given fuzzy sets to each of the fuzzy sets representing individuals of the defined natural-language expressions. The distance between two fuzzy sets A and
B can be calculated as follows:
A
IJ'B{Xi)/Xi Jx
^The euclidean distance between two points (e.g. j and k) in an n-dimensional space can be defined as Bjk = Chapter 5. Reasoning With Imprecise Knowledge 118
\
B R U L E 1 9
IF
Peripheral: (home type is freehold) (application is the wife) (respondent is the husband)
Significant: (applicant financially solvent) (respondent financially insolvent) (divorce has been granted) (there is no dependent children)
Essential: (length of the marriage is very short) (respondent is the owner of the home) (applicant is staying in the home) (respondent is staying in the home) (applicant wants to sell the home) (applicant has no equitable interest in the home) THEN
The wife must leave the matrimonial home immediately and she will not get any share of the equity of the home.
Figure 5.5: The advice and the preconditions of BRULE19
distance(A, B) = y/'^[pA{x) - T=1
Finally, the natural-language expression that yields the shortest distance is accepted as the right linguistic term for use in ASHSD’s reasoning processes.
The application of linguistic variables and fuzzy set theory offers a simple and well- tested way to embed imprecise knowledge in ASHSD. Whenever such knowledge occurs in the language of a rule, the fuzzy treatment is applied. The relevance of these fuzzy linguistic terms to particular items of knowledge are illustrated in Figure 5.4 and Figure
5.5 by using the prediction rules BRULEOl and BRULE19.
This fuzzy knowledge affects ASHSD’s decision-making process in the CBR part also.
We discuss in the next chapter how these linguistic variables affect the procedure of selec tion of similar cases, with respect to a given case, from ASHSD’s case base. Chapter 6
Organisation of Knowledge Base
6.1 Introduction
The knowledge base of ASHSD consists of a rule base and a case base. It uses both of them for the decision-making process. The rule base is partitioned to facilitate domain knowledge representation and improve the computational efficiency during inferencing.
The case base is also partitioned to increase the use of case indexing, which helps with effective retrieval of the right case. ASHSD’s rule base can generate three types of out put: comprehensive advice, partial advice, and no advice. The different rule-base ‘advice’ facilities of ASHSD will be discussed in the next section.
The case base includes a stepwise similarity assessment technique which allows access to and retrieval of similar cases. Each case retrieval is based on a pre-defined scoring mech anism which reflects the similarity of the retrieved case to the new case. A justification facility is also provided to describe the criteria in favour of the suitability of a particular case.
119 Chapter 6. Organisation of Knowledge Base 120
RULE-BASE
RULE-BASEOl RULE-BASE02 RULE-BASE 03
A R U LEO l A R U LE14 ..... A R U L E 28
BRULEOl BRULE02 BRULEOl BRULE62 BRULEKM) BRULEIOI
B RU LE99 B R U L E I60
IF (patternOI) IF (patternOI) IF (patternOI) (pattern02) (pattern02) (pattern02) (pattern03) (pattern03) (pattern03)
(pattern ) (pattern ) (pattern ) T H E N " T H E N " T H E N " (conclusion) (conclusion) (conclusion)
Figure 6.1: rule base structure
6.2 Rule Base Structure
The structure of the rule base Is shown in Figure 6.1. It consists of about 190 rules. It
was founid to be natural to partition ASHSD’s rule base into three parts: RULE-BASEOl,
RULE-BASE02, and RULE-BASE03. RULE-BASEOl consists of all the rules related to
the owned matrimonial home settlement problem, RULE-BASE02 contains all the rules
that handle tenancy-transfer problems, and finally RULE-BASE03 has the necessary rules for severity of injunction. As we have mentioned before in chapter 1 (section 1.6.3), each of
these rules is of one of two different types: available-option(s) rules^ and prediction rules.
We now discuss the different rule-based ‘advice’ that follows from these types of rules.
Rule-Based Advice
The main idea of rule-based advice can be described formally as below: Chapter 6. Organisation of Knowledge Base 121
Let R be a given set of rules in the domain D, and R = Rqi, Rq2 i R 0 3 , Rn- Each rule consists of a set of preconditions and a conclusion which is true when the preconditions are satisfied. The rules can be defined as follows:
R qI — {PC/fQii^ PCIrOi2) PCrOlS^ P C r0 1 4 i ’PCfOlm^ COTlclusionQ\^
R q2 — {-E ^ r0 2 1 ) PCfQ22i PCr023^ PCfQ24) PCrQ2Tni COTlclusioTlQ‘2^
R q 3 — {PCr031 ) PCr032'i PCr033i P C r034i P C r 0 3 m ^ COnclusionQ^}
Rq4 — \^PCrQ4\^ PCyQ42^ PCf043) PCfQ44^ -E^r04mi COTlclusionQ^y
Rn — \^PCfnli PCrn2i PCrn3i PCrn4i PCrn3""PCrnm^
where m and n can be any positive integer. ASHSD’s two types of rule are totally separated in its rule base and they do not interact with each other at the time of rule- based analysis. A user can select rule-based ‘advice’ facility for a new case and the system can presents any of its three types of rule-based advice.
After examining the previously-decided case reports^ and the legal text sources [e.g.
(Cretney, 1992), (Black & Bridge, 1992)], we observed that some of the preconditions of prediction rules were of secondary importance in drawing conclusions and some were of little significance. Hence, the preconditions of prediction rule here fall into three classes: peripheral, significant, and essential. Peripheral preconditions are of secondary importance in drawing a conclusion and are required just to provide information about the context.
Essential preconditions are those that are critical in drawing conclusions. Significant preconditions are those that fall in between essential and peripheral, in that though they are important when drawing the conclusions, they are not critical on their own.
The reason for classifying the preconditions of prediction rules is that this enables the most suitable rules in the scoring mechanism to be ranked, when ASHSD fails to give any clear-prediction. This is done in the scoring mechanism by weighting and normalising the facts in the rules based on the categories of peripheral, significant and essential precondi-
^ See Appendix A for further details. Chapter 6. Organisation of Knowledge Base 122 tions. By using trial and error, and by tuning each class of preconditions, we evolved the weights for peripheral, significant and essential to be 0.2, 0.6 and 0.9 respectively. The use of these weights and their consequences are shown in the example given in section 6.3.3 .
6.2.1 Comprehensive Advice
In comprehensive-ad vice, the system presents the possible available-action (s) plus a clear- prediction of a court’s decision. This clear-prediction presents the appropriate conclusion with justification to the user, cLS for a conventional rule-based expert system with expla nation facilities.
To exemplify the functionality of the system’s rule base, mainly the comprehensive advice^ we take an illustration to show how the system deals with a test case (i.e. M itchell
V M itchell).
Example ;
The M itchell v M itchell case is an example of a case involving an exclusion order from the matrimonial home. This problem shows how the system handles rule-based
'available-action(s)’ and rule-based ‘clear-prediction’. The main facts of the case are as follows:
Mr and Mrs Mitchell are in their early fifties. They were married on 15 April 1982.
Mr Mitchell was 40 years old and Mrs Mitchell 38 years. They have two children, a girl named Naomi who is now nearly 9 years old and a boy named Peter who is 3 years old.
The husband is an accountant and the wife is a physical education instructor in a local further education college. They are financially solvent, but unfortunately differences have arisen between them. They are staying in a rented three-bedroom flat near Richmond station. Mr Mitchell is the official tenant of that flat. Due to the unfortunate difference between them, they had frequent, violent arguments and on a few occasions the little girl was the victim of the violence. Once Mr Mitchell slapped the girl so hard that she was hospitalised immediately and he has threatened to use violence again. Mr and Mrs Chapter 6. Organisation of Knowledge Base 123
Mitchell started living separately in the same flat. The little girl and her brother Peter are living with their mother. In October 1994, Mrs Mitchell petitioned for divorce on the basis of unreasonable behaviour of her husband. She applied to the local county court for an order to exclude her husband from the flat. Mr Mitchell denied all his wife’s allegations.
But there is medical evidence from the local hospital that Naomi was physically assaulted by her father.
In a question-answering session, ASHSD picks up relevant facts of the M itchell v
M itchell case as shown below:
‘exclusion order’, ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the hus band’, ‘respondent is the official tenant’, ‘divorce proceedings are pending’, ‘respondent denies the alleged violence’, ‘respondent has used violence against a child of the fam ily’,
‘evidence of the allegation is corroborated’, ‘severity of the allegation is dangerous’, ‘re spondent has threatened to use violence again’, ‘couple are living separately in the same home’, ‘dependent children are living with the applicant’, ‘appeal for injunction’, ‘length of the marriage is middling’, and ‘there are dependent children’.
According to the above facts, ASHSD presents a comprehensive rule-based advice which consists of the output of the rules ARULE29 and BRULE121. The respective outputs are shown below:
The present situation is triggered the available-action(s) rule ARULE29. Its precondi tions and possible actions are as follows:
PRECONDITIONS OF ARULE29
(applicant is the wife)
(respondent is the husband)
(respondent has used violence against a child of the family)
(respondent has threatened to use violence again) Chapter 6. Organisation of Knowledge Base 124
AVAILABLE-ACTION(S):
The court may make one or both of the following orders:
[1] an order requiring the respondent to leave the matrimonial home.
[2] an order probhibiting the respondent from entering the matrimonial home.
All the preconditions of BRULE121 are matched as a consequence of the facts of
M itc h e ll V M itchell case. Due to this ASHSD provides a clear-prediction which is a pos sible outcome of this case. This preconditions, prediction and justification of BRULE121 are shown below:
PRECONDITIONS OF BRULE121:
peripheral:
(exclusion order)
(home type is rented)
(applicant is the wife)
(respondent is the husband)
(length of the marriage is middling)
significant: Chapter 6. Organisation of Knowledge Base 125
(respondent denies the alleged violence)
(there are dependent children)
(respondent is the official tenant)
(divorce proceedings are pending)
(dependent children are living with the applicant)
essential:
(respondent has used violence against a child of the family)
(evidence of the allegation is corroborated)
(severity of the allegation is dangerous)
(respondent has threatened to use violence again)
PREDICTION
In this case a general eviction order should he granted, and the husband may
be evicted from the house within two weeks of the order. It is ordered that he
should not threaten or use violence against the child. In this case it is also
advised that a power of arrest be granted which would allow the police to arrest
the husband, if the order is broken.
The justification for this recommendation is shown below:
JUSTIFICATION:
The husband denied assaulting the child of the family. However, evidence of
this allegation has been provided and the litigation is corroborated. Therefore,
it has been decided that the husband has to move out of the matrimonial home.
Due to the severity of the assault and the request from the wife, the order for
power of arrest has also been granted. Chapter 6. Organisation of Knowledge Base 126
From the above facts, it is clear that the applicant did not appeal for an arrest order.
But the present triggered rule (i.e. BRULE121) is for an exclusion order in conjunction w ith a power of arrest. Now it is up to the user to decide if that recommended conclusion is acceptable or if some external consideration requires it to be set aside.
In this stage ASHSD’s help facilities can offer some support to remind the user about the discretionary nature of the Domestic Violence and Matrimonial Proceedings Act
(DVMPA) 1976. The output of the help facility for BRULE121 is shown below:
Do you want to know any general legal information that
may serve as a guide to interpretation here ?
1. Yes 2. No
Choice:: 1
According to one Law Commission report^ - ^[T]he DVMPA 1976 broke new ground by enabling the High Court or a county court to attach a power of arrest to an injunction which either restrains one party from using violence or contains an exclusion order. Powers of arrest may also be attached to orders made in proceedings under the Domestic Proceedings and Magistrates’ Court Act 1978 in similar circumstances, although there are a number of minor differences between and uncertainties about the exact scope of these powers. Under the present law, powers of arrest are regarded as relatively exceptional measures, they are normally subject to a time limit of three months and tend to be attached to a minority of injunctions.
[T]he court should be able to attach a power of arrest to any order provided that the respondent had in fact caused actual bodily harm to the victim and the order specified exactly what breaches of the order would give rise to the power of arrest, unless in all
^The Law Commission Report (LAW COM. No. 207), HMSG Publications Centre, London SW8 5DT. Chapter 6. Organisation of Knowledge Base 127 the circumstances it appeared that the applicant or a child would be adequately protected without it’ [(LAW COM. No. 207), p.44]
Next, using the same case (i.e. M itchell v M itchell), we shall describe ASHSD’s case base management and case retrieval techniques in the next section.
6.3 Case Base Management and Case Retrieval
A hypernode case base is a set of interconnected graphs. The case base contains a section of heterogeneous (in size and in structure) cases. The hypernode model provides inherent support for the nesting of information, e.g. a graph whose nodes can themselves be graphs.
This feature gives straightforward support for case abstraction and allows each real object to be represented as a separate graph.
In case-6ased reasoning, a new problem is solved by estimating its similarity to a specific previously-decided problem and then adapting the solution of the known problem to the new one. Similarity depends on the construction of a metric that takes in a number of characteristics (e.g. goals, knowledge, context, common features) of the problem domain.
In general, the performance of CBR systems relies heavily on a proper indexing schema and organisation for their case bases. Our case base consists of two parts: a case library, which serves as a repository for cases, and a set of access procedures.
One of the main issues in case-based reasoning systems development is the formation of a proper case-indexing schema. The indices of a case act like a card catalogue in a library to direct readers towards books that are likely to fulfil their reading needs. Case indices are the salient features, i.e. the choice of features that distinguish a given case from other cases. The indexing problem is one of making sure that a case is accessed whenever appropriate. Much research [e.g.(Goldman et al., 1988), (Kolodner, 1989), (Montazari &
Adam, 1993), (Nitta et ah, 1992)] has been devoted to effectiveness in the area of case indexing. Chapter 6. Organisation of Knowledge Base 128
SELECTION OK OITION MAIN CASE llASE
CHOICE 1 CHOICE 2 C H O IC E 3
INJUNCTION
S T E P 2
SELECT CAStS WITH
Figure 6.2: Representation of steps in the similarity assessment
The relevant similarity is judged by matching of features of the cases. Thus in a matrimonial home settlement case, both the problem and the precedent are characterised in terms of the financial and personal facts; such as the parties’ financial position, the length of stay in the home, the ownership of the home, financial needs, and other social aspects.
The present approach to retrieval based on multiple features involves using a three-step algorithm .
First, the system reacts according to a choice made by the user on an abstraction hier- Chapter 6. Organisation of Knowledge Base 129
archy of the given FIRST JNDEX^d and then selects only cases whose FIRST JNDEXid
matches with the new case. For example, if the new case is dealing with an injunc
tion order (e.g. to exclude a spouse from the matrimonial home), then the first step
is to select all the cases from the case base that have already been decided to include
an injunction.
• The second step is to compare the cases selected from the case base with the new case.
This involves a similarity-assessment module in working out the SECOND JNDEXid
and using it to find the right level in the abstraction hierarchy that is comparable
with the new case. Therefore this module searches for similar cases which have
similar types of surface features. The similarity between two cases is measured by
comparing the main facts and events in each case. If two cases have a long sequence
of common facts or events, the cases are recognised as similar.
• The third step is to identify the most similar three cases with respect to the new
case. Here, the system assesses similarity by comparing and examining the details
under THIRD JNDEXid of the selected cases. It tries to bring out similarity hidden
under the surface description of the selected cases, using knowledge stored in the
case base.
In summary, the process of refinement starts initially when the user makes a choice (in the sense given in section 6.3.1) and the system uses this to retrieve the cases that have some common salient feature(s). The second step is a further selection, this time comparing the new case with the related cases on the similarity measure SECOND JNDEXid. T he final discrimination of cases takes place in the third step where the remaining cases are compared with the new case using a finer measure of similarity, i.e. THIRD JNDEXid.
The three levels of case indexes and their relative importance are closely associated with the three levels of rule preconditions as discussed in the previous section. The schematic representation of stepwise refinement of similarity assessment is shown in Figure 6.2 . Chapter 6. Organisation of Knowledge Base 130
6.3.1 Example : Exclusion Order From a Rented M atrimonial Home
In order to illustrate by an example, we consider the legal problem of the exclusion order.
The Table 6.1 shows the cases that are given unique identifications for use in the case base and all these cases are related to injunction orders.
Case No Case name
caseOl Silverstone v Silverstone [1953] 1 All ER 556
case02 Montgomery v Montgomery [1964] 2 WLR 1036
caseOS Gurasz v Gurasz [1969] 3 WLR 482
case04 Tarr v Tarr [1971] 2 WLR 376
case05 Jones V Jones [1971] 1 WLR 396
case06 Hall V Hall [1971] 1 WLR 404
case07 Phillips V Phillips [1973] 2 All ER 423
caseOS Brent v Brent [1974] 2 All ER 1211
case09 Bassett v Bassett [1975] 1 All ER 513
caselO Walker v Walker [1978] 3 All ER 141
case11 Elsworth V Elsworth [1980] 1 FLR 245
case12 Rennick v Ren nick [1978] 1 All ER 817
case13 Mayers v Mayers [1982] 1 All ER 776
case14 Samson v Samson [1982] 1 All ER 780
Table 6.1: Some of the previously-decided injunction-related cases stored in the case base
First Step of Similarity Assessment
In the first step the user will select the generalised feature ‘severity of injunction’ option from a choice of owned home settlement, tenancy transfer, and severity of injunction. The system will retrieve all the above cases because they relate to injunction. However, even at this stage it is difficult to decide which of the cases represents the closest match. Therefore, the next action is to perform the similarity assessment as described in the second step. Chapter 6. Organisation of Knowledge Base 131
Second Step of Similarity Assessment
In this step, the model of similarity is based on a binary relationship between the SECOND-
IN D E X id hypernodes used. We consider two cases to show how assessment of the similarity or the association between them works. Let SECONDJNDEXOl, SECONDJNDEX02 for this example be the two second-index hypernodes for CASEOl and CASE02 respectively.
The secondJndexes attribute of CASEOl refers to the main surface features. For exam ple, in Silverstone v Silverstone (i.e. CASEOl), the attribute $secondJndexes refers to the surface features ‘home type is owned\ ‘applicant is the wife\ ‘respondent is the hus band’^ ‘respondent has used cruelty against the applicant’ ‘allegation of adultery against the respondent ‘divorce proceedings are pending ‘respondent is the owner of the home
‘respondent left home\ ‘respondent is cohabiting with new partner’^ ‘appeal for injunction’,
‘exclusion order granted’, ‘counter appeal to defy exclusion’, and ‘counter appeal refused’.
Similarly, in M ontgom ery v M ontgom ery (i.e. CASE02), the attribute $secondJndexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’, ‘respondent is the official tenant’, ‘respondent has used cruelty against the ap plicant’, ‘divorce has been granted’, ‘couple are living separately in the same home’, ‘appeal for injunction’, and ‘appeal refused’.
The simplest of all association measures is second.indexoi D SECOND-INDEX 02, which produces 4 shared index elements. In order to take an account of the size difference of the second-index hypernodes, the simple matching coefficient is normalised. (Failure to normalise may lead to counter-intuitive results). Because of this, we have taken the normalised association from both index hypernodes.
The association o f th e second JNDEXoi with respect to second_INDEX 02 is defined as
Sim(SECONDJNDE xoi, SECOND_INDEX02) and the similarity coefficient of SECOND.INDEX02 with respect to secondjndexoi is Sim{SECOND.iNDEX02, SECONDJNDEXOl). T hen the mutual similarity coefficient, tisim{SECONDJNDEXoi, secondJN D E X 02), is the mean of
Sim{SECONDJNDEX 101,SECONDJNDEX02) and Sim{SECONDJNDEX02,SECONDJNDEX01) and can be defined by the equation [6.1]. Chapter 6. Organisation of Knowledge Base 132
CASEOl CASE02 CASE03 CASEIW CASEOS CASE06 CASE07 CASE08 CASE09 CASED) CASED CASE12 CASE 13 CASE 14
CASEOl 0.(K) 0.22 0.04 0.01 0.22 0.02 0.22 0.22 0.02 0.23 0.02 0.05 0,19 0.02
CASE02 0.22 0.00 0.18 0.21 O.IX) 0.20 O.IX) O.IX) 0.20 0.01 0.20 0.17 0.41 0.20
CASE03 0.04 0.1» 0.00 0.03 0.18 0.02 0.18 0.18 0.02 0.19 0.02 0.01 0.20 0.02
CASHM D.DI 0.21 0.03 O.IX) 0.21 0.01 0.21 0.21 0.01 0.22 0.01 0.04 0.20 0.01
CASE05 0.22 0.1X) 0.18 0.21 O.IX) 0.20 O.IX) O.IX) 0.20 0.01 0.20 0.17 0.41 0.20
CASE06 D.D2 0.20 0.02 0.01 0.20 O.IX) 0.20 0.20 O.IX) 0.21 O.IX) 0.03 0.21 O.IX)
CASED? 0.22 O.IX) 0.18 0.21 O.IX) 0.20 O.IX) O.IX) 0.20 0.01 0.20 0.17 0.41 0.20
CASEIW 0.22 O.(X) 0.18 0.21 O.IX) 0.20 O.IX) O.IX) 0.21) 0.01 0.20 0.17 0.41 0.20
CASEOy 0.02 0.20 0.02 0.01 0.20 O.IX) 0.20 0.20 O.IX) 0.21 0.1X1 O.IX) 11.03 0.21
CASED) 0.23 0.01 0.19 0.22 0.01 0.21 0.01 0.01 0.21 O.IX) 0.21 0.18 0.42 0.21
CASED 0.02 0.20 0.02 0.01 0.20 O.IX) 0.20 0.20 O.IX) 0.21 O.IX) 0.03 0.21 O.IX)
CA SE 12 0.05 0.17 0.01 0.04 0.17 0.03 0.17 0.17 O.IX) 0.18 0.03 O.IX) 0.24 0.03
CASE 13 0.19 0.41 0.20 0.20 0.41 0.21 0.41 0.41 0.03 0.42 0.21 0.24 O.IX) 0.21
CASE14 0.02 0.20 0.02 0.01 0.20 O.IX) 0.20 0.20 0.21 0.21 O.IX) 0.03 0.21 O.IX)
Table 6.2: The relative similarity distance for injunction cases
^i,,rn{SECONDJNDEXOI,SECONDJNDEX02)= - [ 5 (^ + 5 ^ ] ...... [6 .1]
where Sir = Sim.(SECOND JNDEXOl,SECOND JN D E X02)
and Sri = Sim(SECONDJNDEX02,SECONDJNDEX0\)
1. if nsrm{SECONDJNDEX0l, SECOND JNDEX02) = 1, then the two indexes SECOND.
INDEXOl, SECOND JNDEXQ2 are exactly similar.
2. if ps,m{SECONDJNDEXO\,SECONDJNDEXt)2) = 0, then the two indexes SECOND.
INDEXOl, SECOND.INDEX02 are completely dissimilar.
3. As the value oï nsim{SECOND.INDEXO\,SECOND.INDEX02) approaches 1, the two
indexes SECOND.INDEXOl, SECOND.INDEX02 become more similar.
4. As the value of nsim[SECOND.INDEXOl, SECOND.INDEXQ2) approaches 0, the two
indexes SECOND.INDEXOI, SECOND.INDEX02 become less similar. Chapter 6. Organisation of Knowledge Base 133
Third Step of Similarity Assessment
The final step of similarity measurement is the assessment of THIRD JNDEXid for a se lected number of cases from the second step. The THIRD JNDEXid refers to the most dis tinctive characteristic(s) associated with the case. In this project, we have chosen five at tributes which identify the final similarity measure for all selected cases for owned home set tlement, These attributes are $number_of^dependent-children, Slength-ofstayJnJhe-matri monialJiome^ $number-ofstep-children^ $number-of-divorce-offspring^ and $length-of-mar riage. In the tenancy transfer and severity of injunction cases we have consider two at tributes which help us in determining the final similarity: $number-of-dependent-children, and $length-of-marriage.
In the third step we have a weighted scoring mechanism. We calculate the score, for each selected case from the second step, by using equation [6,2],
TScasci = w \n d c + w^lsm h -f wsnsc -\- w^ndo + w jm ,[6 ,2]
w here
Third score for the case,,
Wi Weighted factor for number of dependent children,
ndc Number of dependent children,
W2 Weighted factor for length of stay at matrimonial home,
Ism h Length of stay at matrimonial home,
W3 Weighted factor for number of stepchildren,
nsc Number of stepchildren.
IÜ4 Weighted factor for number of divorce offspring,
ndo Number of divorce offspring.
Ws Weighted factor for length of marriage,
Im Length of marriage.
Following this we can calculate the measure of similarity distance of all selected cases in the third step with respect to the new case. Chapter 6. Organisation of Knowledge Base 134
0.23 CASEOl CASEIO
0.42
0.41 0.41 CASE02 CASE13 CASEOS
0.24 0.41 0.41 CASE12
CASE07
CASES WHERE CASEOS ABSOLUTE DISTANCE > 0.22
Figure 6.3: Diagrammatic representation of the relative distances between cases
Measuring Similarity Distances
Once the THIRD JNDEX,d values for the selected cases are calculated by using equation
[6.2], we calculate the relative distance between the cases by taking the absolute difference of third-index scores of the respective cases. The absolute distances that we compute for the cases from Table 6.1 are shown in a tabular form in Table 6.2 and the diagrammatic representation (the relative distances are not to scale) is shown in Figure 6.3. This is to demonstrate informally that the above graphical case representation captures the real- world legal knowledge. If the set of encoded cases is considered as a graph, then given a minimum distance ‘d’, we can find a subgraph consisting of a node N, all nodes distant d or less from N, and the edges from the original graph that join pairs of the nodes thus identified. These subgraphs allow us to identify similar cases. Splitting the resulting subgraph in a similar way produces progressively finer categorisations. Chapter 6. Organisation of Knowledge Base 135
6.3.2 Selection of Similar Cases for a New Case
Let us consider the Mitchell v Mitchell situation (i.e. CASE82) of a couple described in section 6.2.1 of this chapter. By relying on the information above, we shall describe how to use the previously-mentioned three-step algorithm, as shown in Figure 6.2, to identify the most similar cases in the case base.
First step of similarity assessment
Since this is an injunction-order-related case, the system will retrieve only those cases that relate to injunction order. However, even at this stage it is difficult to decide which of the cases represents the closest match. Therefore, the next action is to perform the similarity assessment as described in the second step.
Second step of similarity assessment
In this step, the model of similarity is based on a binary relationship between the SECOND.
INDEXrd hypernodes used. From the above example, we have selected two cases to assess the similarity or the association between them. Let SECONDJNDEX32, SECONDJNDEX04 for this example be the two second-index hypernodes for CASE82 and CASE04 respectively.
In Mitchell v Mitchell (i.e. CASE82), the attribute $secondJndexes refers to the surface features ‘home type is rented\ ‘applicant is the wife’, ‘respondent is the husband’,
‘respondent is the official tenant’, ‘divorce proceedings pending’, ‘respondent denies the alleged violence’, ‘respondent has used violence against a child of the fam ily’, ‘evidence of the allegation is corroborated’, ‘severity of the allegation is dangerous’, ‘respondent has threatened to use violence again’, ‘couple are living separately in the same home’,
‘dependent children are living with the applicant’, and ‘appeal for injunction’.
Similarly, in Tarr v Tarr (i.e. CASE04), the attribute SsecondJndexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’,
‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘di Chapter 6. Organisation of Knowledge Base 136 vorce has been granted \ ‘dependent children are living with the applicant \ ‘couple are living separately in the same home\ ‘appeal for injunction’, ‘exclusion order refused’, ‘question on jurisdiction to exclude the legal tenant’, and ‘exclusion of the legal tenant is confirmed by the court of appeal’.
We can see from the above that the new case shares 7 main surface features with th e CASE04, therefore the mutual similarity measure is 0.5608. In the same way each retrieved case can be compared with the new case and a measure of mutual similarity can be obtained. In our example the values obtained for all retrieved cases are shown in
Table 6.3 .
In order to obtain the third similarity measure, we choose all the cases whose values on the mutual similarity measure are higher than 0.5. In our example, CASE02 and CASE04 will be chosen for the third step of similarity measure.
Case name Second Index Size No of shared features P'sim coefficient
caseOl 13 3 0.2307
case02 9 6 0.5640
case03 12 4 0.3204
case04 12 7 0.5608
case05 14 6 0.4450
case06 12 5 0.4006
caseO? 12 6 0.4807
caseOS 12 6 0.4807
case09 12 4 0.3204
case10 14 5 0.3708
e a s e l1 12 4 0.3204
case12 9 4 0.3760
case13 12 5 0.4006
casel4 13 4 0.3076
Table 6.3: The mutual similarity coefficients of the above injunction-related cases Chapter 6. Organisation of Knowledge Base 137
Third step of similarity assessment
Following this we can calculate the measure of relative similarity of all selected caaes in the third step with respect to the new case, as discussed in the previous subsection. The input-output behaviour of ASHSD is indicated below:
The most suitable cases and their relative ratings, with respect to the
present case, are ::
[1] CASE04 0.01
[2] CASE02 0.20
[J] Justification
[P] Previous Menu
Enter Your Option ::
On entering the option J (i.e. for justification for the best selected cases), the next menu will appear as below::
Enter CASE Serial number for justification ::
[1] CASE04 0.01
[2] CASE02 0.20
Enter Option :: 1
On selection of option number 1 (i.e. selecting CASE04), the user is presented with the justification as given below. The output is produced by substitution of particular information about the current problem in a general template for the presentation of the argum ent. Chapter 6. Organisation of Knowledge Base 138
Justification
You have chosen to find out how CASE04 was selected as being similar to the
present case.
In the present case, the applicant requested an injunction order. Therefore,
all the cases retrieved from the case base were related to injunction order.
Then the main facts and events (i.e. surface features) of the present case were
extracted and compared with the surface features of CASE04.
In this particular circumstance, the surface features of the present case
were ::
(home type is rented)
(applicant is the wife)
(respondent is the husband)
(respondent is the official tenant)
(divorce proceedings pending)
(respondent has denies the alleged violence)
(respondent has used violence against a child of the family)
(evidence of the allegation is corroborated)
(severity of the allegation is dangerous)
(respondent has threatened to use violence again)
(couple are living separately in the same home) Chapter 6. Organisation of Knowledge Base 139
(dependent children are living with the applicant)
(appeal for injunction)
and the surface feature of CASE04 were ::
(home type is rented)
(applicant is the wife)
(respondent is the husband)
(respondent is the ojficial tenant)
(respondent has used cruelty against the applicant)
(divorce has been granted)
(dependent children are living with the applicant)
(couple are living separately in the same home)
(appeal for injunction)
(exclusion order refused)
(question on jurisdiction to exclude the legal tenant)
(exclusion of the legal tenant is confirmed by the court of appeal)
By comparing the common features of both cases and standardising the
measure, the similarity coefficient was obtained, with value 0.5608.
You can see from the output above which features of today’s situation are
shared with CASE04. If the best answer for today’s situation deviates from
the judgement in CASE04, this will be due to the current surface features :: Chapter 6. Organisation of Knowledge Base 140
(respondent has used cruelty against the applicant)
(divorce has been granted)
(exclusion order refused)
(question on jurisdiction to exclude the legal tenant)
(exclusion of the legal tenant is confirmed by the court of appeal)
If you cannot draw any immediate conclusion from this, you may like to see
the nsimilar outputs for the other cases that have been identified as somewhat
relevant. You can then consider what surface features in the list like the one
immediately above are correlated with what judgements, and think about the
implications of such information.
Since the similarity coefficient of the present case was above the threshold
value 0.5 which has proved to be a reasonable cut-off in case selection, that
case was selected for the next stage.
Each case that reached this stage was given a weighting. The weighting was
based on a number of attributes and the emphasis given to them by legal
decision-makers in case reports. The attributes were ::
(number of dependent children)
(length of marriage)
The weighted score on those items for the present case is 0.52 and the weighted Chapter 6. Organisation of Knowledge Base 141
score for the CASE04 is 0.51. The distance between the cases on this scale,
where 0.0 would mean a total correspondence and 0.5 or more would indicate
no significant resemblance, is 0.01.
The above explanation is intended to give some justification for the reasons for select ing the particular case. Using the comprehensive rule-based advice and the outcomes of previously decided similar cases, the user has to find a reasonable solution for the present case.
The question of partial rule-based advice arises when the system fails to find a clear- prediction for a case. In the next section we shall describe ASHSD’s partial rule-based advice facilities.
6.3.3 Partial Advice Mechanism
The partial rule-based advice consists of suggestion on available-action (s) and a type of prediction which may be a speculation or no-prediction. We have already mentioned that the prediction rules can provide any of the three types of output: clear-prediction^ specu lation, and no-prediction. ASHSD presents speculation by applying a weighting criterion to the rule preconditions that are true, even when none of the prediction rules is triggered.
A speculation consists of conclusions that would have followed if all the preconditions of a rule that has some relevance has been true, plus output focusing on the failed precondi tions (i.e. reasons why a conclusion cannot be accepted without reservations). The first step in generating a speculation is to identify the rules that are nearly triggered. A scoring mechanism is used to determine which rules are closest to triggering. We have found by experiment that there is a consistent threshold in our score, below which any information that ASHSD may give is unhelpful. The system, therefore, offers no-prediction unless at least one of its prediction rules has a score above the threshold value 0.35. Thus the partial rule-based advice can be in one of two categories, based on the value of the score. Chapter 6. Organisation of Knowledge Base 142
Category-one Partial Rule-based Advice
In the category-one of partial rule-based advice, ASHSD offers the relevant available- option(s) and also presents a speculation. When no prediction rule triggers, the question of speculation arises. Speculation is generated from the prediction rules that are closest to triggering. For each of these rules a justification is provided, describing the conclusion that would have been certain if it had triggered, and which parts of the rule did or did not m atch.
The first step in generating a speculation is to identify the suitable rules which are nearly triggered as a consequence of the facts of the new case. A scoring mechanism is used to find out which rules are closest to triggering. Three of the nearly-triggering rules are chosen by weighting the preconditions as peripheral, significant and essential. The weight-based normalised scoring mechanism that is used to order the rules is given below:
......
where Score^ = 4- T wgTVp and Scorei is the total number of preconditions of the rule in the equation. Ag, A^, Np are the number of essential, significant and peripheral preconditions that are true for the current case. The weighting factors tci, and W3 are for the essential, significant and peripheral categories of preconditions. We have not observed more than just these three categories in the legal literature. For any topic where the observed number C of classes is different, the numerator of equation [6.3] should change to a sum of C terms, with theC weights determined by experiment. This is a very simple approach to the question of how to make reasonable use of the knowledge expressed in rules even when they would fail to be triggered in an ordinary expert system.
Certainly one can imagine applications where the approach would be too simple, but in the last resort one can not be sure of how well it will behave, from a user’s viewpoint, without trying it out.
To illustrate the functionality of ASHSD’s rule base, particularly the category-one of partial rule-based advice, we show what happens when a particular test case (M iller v Chapter 6. Organisation of Knowledge Base 143
M iller) is used.
E x a m p le :
The M iller v M iller case is an owned matrimonial home settlement case. This problem serves as an example of how ASHSD tackles rule-based partial suggestion. The litigation has had a long and rather sad history. The bare facts of this case are as follows:
Alan Miller and Janet Miller were married on 2 February 1985. Mr Miller was working as a clerical assistant and Mrs Miller was a high school teacher. In October 1985, Mr Miller bought a two-bedroom flat for £45,000 in central London. The flat was their matrimonial home. The purchase was financed by Mr Miller and in part by a building-society mortgage.
Mr Miller paid the mortgage payments. There was a history onf arguments throughout this childless marriage. In May 1993, there was an horrendous incident of violence leading to Mr. Miller needing hospital treatment. That was one of the incidents of the violent wife who assaulted Mr Miller. The marriage broke down and Mr Miller left home and started cohabiting with another woman in a separate accommodation. Mrs Miller is financially solvent, but Mr Miller is not. Mrs Miller was living in the matrimonial home. The court granted a divorce order in June 1995. She then applied to the court to permit her to sell the matrimonial home and claim a suitable share of the equity of the proceeds of the sale.
In a question-answering session, ASHSD gathers facts of a particular case. The facts of the M iller v M iller case are as below:
^owned home adjustment^ ‘home type is freehold’^ ‘applicant is the wife\ ‘respondent is the husband\ ‘respondent is the owner of the home\ ‘respondent left home\ ‘respondent financially insolvent’, ‘applicant financially solvent’, ‘applicant is staying in the home’,
‘applicant behaved very badly’, ‘respondent is staying in a separate accommodation’, ‘re spondent is cohabiting with the new partner’, ‘divorce has been granted’, ‘applicant wants to sell the home’, ‘there are no dependent children’, ‘length of the marriage is somewhat m iddling’, and ‘length of stay in the matrimonial home is 10 years’.
When a user selects the rule-based advice mode of ASHSD for this case, the system Chapter 6. Organisation of Knowledge Base 144
B R U L E 2 2
Peripheral: (owned home adjustment) (home type is freehold) (applicant is the wife) (respondent is the husband)
Significant: (applicant is financially solvent) (respondent is financially insolvent) (divorce has been granted) (there are no dependent children)
Essential: (length of the marriage is somewhat middling) (respondent is the owner of the home) (applicant is living in the home) (respondent is living in the home) (applicant behaved very badly) (applicant wants to sell the home) (applicant has no equitable interest in the home)
Figure 6.4: Classification of preconditions of BRULE22
provides the possible action (s) and fails to give a clear-prediction on the basis of these facts. These two types of rule-based analysis are as follows:
The present situation has triggered the rule ARULE03 of available-action (s) for the court. Its preconditions and possible action(s) are as follows:
PRECONDITIONS OF ARULE03
(applicant is the wife)
(respondent is the husband)
(divorce has been granted)
(applicant appealed for home settlement) Chapter 6. Organisation of Knowledge Base 145
AVAILABLE-OPTION(S):
The court may make one or more of the following orders for the present case:
[1] an order that a party of the marriage shall transfer to the other party,
to any child of the family or to such person as may be specified in the
order for the benefit of such a child such property as may be so specified,
being property to which the first-mentioned party is entitled, either in
possession or reversion;
[2] an order that a settlement of such property as may be so specified, being
property to which a party to the marriage is so entitled, be made to the
satisfaction of the court for the benefit of the other party to the marriage
and of the children of the family or either or any of them;
[3] an order varying for the benefit of the parties to the marriage and of the
children of the family or either or any of them any ante-nuptial or post
nuptial settlement (including such a settlement made by will or codicil)
made on the parties to the marriage;
[4] an order extinguishing or reducing the interest of either of the parties to
the marriage under any such settlement; subject, orders for a transfer of
property in favour of children who have attained the age of eighteen.
However, considering all the relevant facts of the above case, none of the second type of rules (prediction rules) is triggered. In this situation ASHSD can generate a ‘speculation’.
SPECULATION: Chapter 6. Organisation of Knowledge Base 146
The first step of any speculation is to identify suitable prediction rules from the rule base using the above mentioned scoring mechanism. In the next step, ASHSD deter mines the three rules that are closest (depending on the score) to triggering, which are
BRULE22, BRULE21, and BRULE20. In order to obtain a speculation, the system selects these rules and compares the scores against a threshold value of 0.35. A value of 0.35 or above indicates, in our experience, that the rule has some significant ability to provide a contribution towards advice that the user is likely to find helpful. If the scores of all the selected rules fail to achieve this threshold value, then the rule base cannot make any useful prediction, and the output of ASHSD indicates accordingly.
Next, we show how ASHSD calculates the score for the selected rules. The classification of BRULE22’s preconditions is shown in Figure 6.4. In ASHSD, the weighting factors for peripheral, significant, and essential are 0.2, 0,6 and 0.9 respectively. Using equation 6.3,
ASHSD calculates the score for BRULE22 as follows;
The value of Scorcu is = 5 x 0.9 -|- 4 x 0.6 + 4 x 0.2
= 4.5 4- 2.4 + 0.8
= 7.7
The value of Scorei is = 15
Scorcfi^^ = 0.5133
In the same way ASHSD calculates scores for all other rules from RULE-BASEOl since the current problem is an instance of owned matrimonial home settlement problems. In the next step, ASHSD selects the three rules with the highest scores (in ascending order).
The summary of the rule-based speculation for the Miller v Miller case is shown below:
The most suitable rules and their relative ratings are ::
[1] BRULE22 0.5133
[2] BRULE21 0.3933
[3] BRULE20 0.3571 Chapter 6. Organisation of Knowledge Base 147
[Press P for Previous Menu]
Enter the option for which you
want further information ::
On selection of option number 1 (i.e. selecting BRULE22), the user is presented with the information as follows:-
If BRULE22 had matched completely with your case, then the clear-prediction from the
rule base would have been ::
PREDICTION;
The wife must leave the matrimonial home immediately and she will not get
any share of the proceeds from the sale of the matrimonial home.
The justification for this recommendation is shown below:
JUSTIFICATION:
In this case the husband is insolvent and he is the owner of the matrimonial
home. The wife is solvent and behaved very badly with the husband. There are
no dependent children of this couple. Therefore, the advice is not to give the
wife any share of the equity of the matrimonial home and she has to leave the
matrimonial home immediately.
The above rule is not an exact match for the information provided. But it is one of the closest to matching, and the facts that have matched and those that have not matched are as follows: Chapter 6. Organisation of Knowledge Base 148
Facts Matched ::
(owned home adjustment)
(home type is freehold)
(applicant is the wife)
(respondent is the husband)
(applicant financially solvent)
(respondent financially insolvent)
(divorce has been granted)
(there are no dependent children)
(length of the marriage is middling)
(respondent is the owner of the home)
(applicant is living in the home)
(applicant wants to sell the home)
Essential facts not matched are::
(respondent is living in the home)
(applicant has no equitable interest in the home)
On the basis of this information, we can see from the output above which features of the new case are shared with BRULE22. If the best answer for today’s situation deviates from the clear-prediction in BRULE22, this will be due to the following surface features::
(respondent is living in the home)
(applicant has no equitable interest in the home) Chapter 6. Organisation of Knowledge Base 149
The above output is designed to help the user to arrive at a reasonable decision. Using the same case (i.e. M iller v M iller), we shall describe how ASHSD retrieves the most similar cases, in the next section.
6.3.4 Example: Owned Matrimonial Home Settlement After Divorce
In order to illustrate by an example, we consider the legal problem of owned matrimonial home settlement after divorce. The cases, in Table 6.4, are given unique identifications for use in the case base.
Steps of Similarity Assessment
In the first step, the system will retrieve all the cases shown in Table 6.4 because they relate to owned home settlement. However, even at this stage it is difficult to decide which of the cases represents the closest match. Therefore, the next action is to perform the similarity assessment as described in the next step. In the last step, we have a weighted scoring mechanism. We calculate the score, for each selected case from the second step, by using equation [6.3].
6.3.5 Selection of Similar Cases for a New Case
Let us consider the M iller v M iller situation (i.e. CASE93) described previously in sec tion 6.3.3. Using the above information, we now explain how ASHSD uses the previously- mentioned three-step algorithm, as shown in Figure 6.2, to identify the most similar cases in the case base. Chapter 6. Organisation of Knowledge Base 150
Case No Case Name
case15 Hanlon v Hanlon [1978] 2 All ER 889
case 16 Browne (formerly Pritchard) v Pritchard [1975] 3 All ER 721
easel? Martin v Martin [1978] Fam 12
case 18 Clutton V Clutton [1991] 1 FLR 242
case19 Goodheld v Goodheld [1975] Transcript No. 269, C.A.
case20 Brown v Brown [1982] 3 FLR 161
case21 Chadwick v Chadwick [1985] FLR 606
case22 Mortimer v Mortimer-Grifhn [1986] 2 FLR 315
case23 Pettitt V Pettitt [1969] 2 All ER 385
Table 6.4: Some of the previously-decided owned home settlement cases stored in the case base
First step of similarity assessment
Since this is an owned home settlement case, the system will retrieve only those cases that relate to owned home settlement. The next step is to perform the similarity assessment as described in the second step.
Second step of similarity assessment
In this step, the model of similarity is based on a binary relationship between the SECOND-
INDEXid hypernodes used. From the above example, we have selected two cases to assess the similarity or the association between them. Let SECONDJNDEX35, SECONDJNDEX22 for this example be the two second-index hypernodes for CASE93 and CASE22 respectively.
In M iller v M iller (i.e. CASE93), the attribute $secondJndexes refers to the surface features ‘home type is freehold’^ ‘applicant is the wife\ ‘respondent is the husband\ ‘respon dent is the owner of the home\ ‘respondent left home\ ‘respondent financially insolvent^
‘applicant financially solvent’, ‘applicant is living in the home’, ‘applicant behaved very badly’, ‘respondent is living in a separate accommodation’, ‘respondent is cohabiting with Chapter 6. Organisation of Knowledge Base 151
CASE15 CASE16 CASE 17 CASE 18 CASE19 CASE20 CASE21 CASE22 CASE:
CASE15 0.00 0.13 0.18 0.09 0.04 0.03 0.38 0.46 0.46
CASE16 0.13 0.00 0.31 0.22 0.17 0.16 0.25 0.33 0.33
CASE 17 0.18 0.31 0.00 0.09 0.14 0.15 0.56 0.64 0.64
CASE 18 0.09 0.22 0.09 0.00 0.05 0.06 0.47 0.55 0.55
CASE19 0.04 0.17 0.14 0.05 0.00 0.01 0.42 0.50 0.50
CASE20 0.03 0.16 0.15 0.06 0.01 0.00 0.41 0.49 0.49
CASE21 0.38 0.25 0.56 0.47 0.42 0.41 0.00 0.08 0.08
CASE22 0.46 0.33 0.64 0.55 0.50 0.49 0.08 0.00 0.00
CASE23 0.46 0.33 0.64 0.55 0.55 0.49 0.08 0.00 0.00
Table 6.5; The relative similarity distance for owned home settlement cases
the new partner\ ‘divorce has been granted\ ‘applicant appealed for property adjustment order \ and ‘applicant wants to sell the home\
Similarly, in M o r tim e r v M o rtim e r (i.e. CASE22), the attribute $secondJndexes refers to the surface features ‘home type is freehold', ‘applicant is the wife\ ‘respondent is the husband’, ‘respondent is the owner of the home’, ‘respondent left home’, ‘broken marriage’, ‘divorce has been granted’, and ‘question on Mesher type order’.
We can see from the above that the new case shares 6 main surface features with
CASE22, therefore the mutual similarity measure is 0.5892. In the same way each retrieved case can be compared with the new case and the similarity measure can be obtained. In our example the values obtained for all retrieved cases are shown in Table 6.6 .
In order to obtain the third similarity measure, we choose all the cases whose values on the mutual similarity measure are higher than 0.50. In our example, CASE17, CASE19, and CASE22 are so chosen for the third step. Chapter 6. Organisation of Knowledge Base 152
Case nam e Second Index Size No of shared features PsiTTi coefficient
case15 13 6 0.4450
casel6 16 5 0.3348
easel? 12 7 0.5416
case18 15 6 0.4142
case 19 12 9 0.6964
case20 12 4 0.3095
case21 9 4 0.3650
case22 8 6 0.5892
case23 9 5 0.4563
Table 6.6; The mutual similarity coefficients for the above cases
Third step of similarity assessment
Following this, we can calculate the measure of similarity distance of all selected cases in the third step with respect to the new case, as discussed in the previous subsection. The output of ASHSD in this situation is now given.
The most suitable cases and their relative ratings, with respect to the present
case, are ::
[1] CASE22 0.16
[2] CASE19 0.36
[3] CASE17 0.50
[J] Justification
[P] Previous Menu
Enter Your Option ::
On entering the option J (i.e. for justification for the best selected cases), the next menu will be as below:: Chapter 6. Organisation of Knowledge Base 153
Enter CASE Serial number for justification
[1] CASE22 0.16
[2] CASE19 0.36
[3] CASE17 0.50
Enter Option ;; 1
On selection of option number 1 (i.e. selecting CASE22), the user is presented with the justification as follows
JUSTIFICATION ::
You have chosen to find out how CASE22 was selected as being similar to
the present case.
In the present case, the applicant requested an owned home adjustm ent
order. Therefore, all the cases retrieved from the case base were related to
owned home adjustment order.
Then the main facts and events (i.e. surface features) of the present case were
extracted and compared with the surface features of CASE22.
In this particular circumstance, the surface features of the present case
were :: Chapter 6. Organisation of Knowledge Base 154
(home type is freehold)
(applicant is the wife)
(respondent is the husband)
(respondent is the owner of the home)
(respondent left home)
(respondent financially insolvent)
(applicant financially solvent)
(applicant is living in the home)
(applicant behaved very badly)
(respondent is living in a separate accommodation)
(respondent is cohabiting with the new partner)
(divorce has been granted)
(applicant appealed for property adjustment order)
(applicant wants to sell the home)
and the surface feature of CASE22 were ::
(home type is freehold)
(applicant is the wife)
(respondent is the husband)
(respondent is the owner of the home)
(respondent left home)
(respondent is staying in a separate accommodation)
(broken marriage)
(divorce has been granted)
(question on Mesher type order) Chapter 6. Organisation of Knowledge Base 155
By comparing the common features of both cases and standardising the
mecLsure, the similarity coefficient was obtained, with value 0.5892,
You can see from the output above which features of today’s situation are
shared with CASE22. If the best answer for today’s situation deviates from
the judgement in CASE22, this will be due to the current surface features::
(broken marriage)
(question on Mesher type order)
If you cannot draw any immediate conclusion from this, you may like to see
the similar outputs for the other cases that have been identified as somewhat
relevant. You can then consider what surface features in the list like the one
immediately above are correlated with what judgements, and think about the
implications of such information.
Since the similarity coefficient of the present case was above the threshold value
0.5 which has proved to be a reasonable cut-off in case selection, that case was
selected for the next stage.
Each case that reached this stage was given a weighting. The weighting was
based on a number of attributes and the emphasis given to them by legal
decision makers in case reports. The attributes were :: Chapter 6. Organisation of Knowledge Base 156
(number of dependent children)
(length of stay in the matrimonial home)
(number of stepchildren)
(number of divorce offspring)
(length of marriage)
The weighted score on those items for the present case (CASE93) is 0.70 and
the weighted score for the CASE22 is 0.86 . The relative similarity distance
between these cases is 0.16 .
The above explanation is intended to justify the reasons for selecting the particular case. We have tried to prepare a template for case-specific justification so that the ex planation appears fairly natural and plausible to the user, though we appreciate that the numerical measures of distance and similarity may not be intuitive means of giving information to non-scientific users.
Although it is not stated to the user via the template above, the threshold value was determined by actual checks of relevance of retrieved material at a late stage of the knowledge-acquisition process.
Category-two Partial Rule-Based Advice
T he category-two of partial rule-based advice provides no-prediction, but it does suggest some valid available-action (s). To give an example of how category-two partial advice works, we can use the same M iller v M iller case. However, let us assume that some of the facts of M iller v M iller case are not true; also that none of the prediction rules has a score above the threshold (i.e. 0.35). In this situation ASHSD suggest only the possible available-action(s) appropriate for the situation; but it refuses to provide any speculation or clear-prediction. Chapter 6. Organisation of Knowledge Base 157
6.3.6 No Rule-Based Advice
ASHSD provides no rule-based advice at all when it fails to suggest any available-action (s) or any kind of predictive information.
6.3.7 No Suggestion from Rule Base and Case Base
ASHSD is set up to provide no suggestion when no rule (i.e. available-action (s) rule and prediction rule) from the rule base is triggered and the scores of all prediction rules are less than the threshold value 0.35. In the case-based reasoning part, ASHSD refuses to retrieve any case when the mutual similarity coefficients for all the relevant stored cases are less than 0.5, and indicates this situation in its output. The threshold values for rule scores
(i.e. 0.35) and case scores (i.e. 0.5) are derived empirically from extensive experiments with retrieval followed by examination of the apparent relevance of the retrieval material, as described at the end of section 6.3.4.
The above examples cover the different kinds of behaviour of ASHSD concerning rule- based advice (i.e. comprehensive advice, partial advice, or no-ad vice at all) and retrieval of previously-decided similar cases. ASHSD has, further, the ability to assess the relative suitability of the two particular reasoning methods (CBR or RBR) for a new problem. We discuss this issue in the next section.
6.4 Automated Decision on the Qualities of the Reasoning M ethods
When no particular preference of reasoning (RBR or CBR) is indicated by the user, ASHSD applies each method (i.e. rule-based reasoning based on the prediction category of rules, and case-based reasoning) separately, and presents results computed via an automated relative rating of the qualities of the RBR and CBR advice. The relevant similarity is judged by matching the features of the selected best case and best prediction rule with the new case. The system uses a similarity-scoring mechanism to determine the suitability Chapter 6. Organisation of Knowledge Base 158 of any reasoning method.
In order to illustrate the functionality of the automated prediction facility, we consider the example of CASE92. Let us assume that according to the facts of this case, BRULEll from the rule base is triggered. The CBR part of ASHSD is used to retrieve the most similar cases, and these are CASE22, CASE23 and CASE21.
The mutual similarity coefficient of the best rule (BRULEll) and the new case (CASE92) is calculated, and the value Rscore is 0.9411. The mutual similarity coefficient of the best case (CASE22) and the new case (CASE92) is 0.4492, which we call Cscore’ According to
ASHSD’s inbuilt algorithm, the advice would be as follows:
The present case is suitable for rule-based reasoning only. The case-based rea
soning is not suitable because there is no suitable case in the case base which
can help to form any useful tentative advice.
If we consider another example, where the Rscore is 0.2567 and the Cscore is 0.9878,
ASHSD’s automated opinion would be as follows:
The present case is suitable for case-based reasoning only. The rule-based rea
soning is not suitable because no rule is fired, and there are no rules with a high
enough proportion of true preconditions to be helpful for the current problem.
ASHSD can generate other types of automated opinions also, by calling on our assess ment of relative suitabilities of prediction rules and cases for different combinations of rule and case scores. This issue is discussed in some detail in the next chapter (section 7.2). Chapter 7
Empirical Observations and Validation of ASHSD
7.1 Introduction
This chapter presents an account of a selection of typical empirical observations and the contents of our experiments. These occur in the next two sections. The chapter ends with accounts of the validation process and the knowledge base maintenance aspects applied to
ASHSD.
Validation is an integral part of any software development project and many studies have been made in the knowledge-based area [(Allen & Saxon, 1987), (Coenen & Bench-
Capon, 1993)]. The common goal of these studies is to assert and even to guarantee the
‘good quality’ or the ‘correctness’ of a piece of software. In our validation exercise, we have used a legal expert to test and comment on our work. But, we found legal experts to be expensive and not readily available within a reasonable time. As a result, we were not able to validate the system with more than one legal expert. Nevertheless, there were some details of the generally-accepted approach to validation that we were able to apply.
These are discussed in section 7.4.
159 Chapter 7. Empirical Observations and Validation of A5H5D 160
7.2 Why Empirical Observation ?
Our overall aim was to examine the relative suitability of each reasoning method (CBR or RBR) in the implemented system. In this experiment only the second type of rules
(i.e. prediction rules) were taken into consideration in the RBR side to find the best rule for predicting a possible outcome for a new case. In order to achieve the aim, a simple scoring mechanism is used in deciding the relative appropriateness of the different reasoning methods for the case in hand.
7.2.1 Empirical Observations
In order to obtain the simplest kind of scheme for inspecting the suitability of any reasoning method, we have devised a similarity-scoring mechanism. The relevant similarity is judged by matching of features of the selected best case and best prediction rule with the new case.
In choosing the main features of a case, the FIRST JNDEXa^ SECOND JNDEXid and th e THIRDJNDEXid hypernodes are used. As we have mentioned before that the preconditions of a second category rule is divided into non-significant, significant, and essential classes. A mutual similarity coefficient (determined by the equation 6.1 in chapter 6) is used to measure the similarity of a new case against the best selected case from the case base. The same similarity-measuring method is used to compute the similarity coefficient of the new case against the selected most suitable rule from the rule base. By comparing these similarity coefficients (for the best case and for the best prediction rule) one should ideally be able to give automated advice on the suitability of either a particular method, or both methods, for a new case. As we will see from the discussion later in this chapter, it is possible to realise this aim.
7.2.2 Example: The Overall Suitability of any Particular Reasoning M ethod
To illustrate the way in which ASHSD indicates the suitability of any specific reasoning method, we have used both real (i.e. previously-decided) and hypothetical test cases. The Chapter 7. Empirical Observations and Validation of ASHSD 161 test cases are shown in Table 7.1 and Table 7.2. Cases in Table 7.1 are real and have been collected from different legal sources^. Two different sets of hypothetical test cases are included in Table 7.2. One of these sets consists of test cases that are modified versions of some of the real test cases from Table 7.1. These hypothetical test cases have the same names as in the original cases (to indicate their origins) and an extra hash mark after their name. The second set of the hypothetical test cases, from CASE76 through CASEIOO in
Table 7.2, are newly generated. For each hypothetical test case, we have constructed the relevant advice. To illustrate the scoring mechanism, we consider the Jackson v Jackson case as follows:
CASE76: Jackson v Jackson
Mr and Mrs Jackson are in their early forties. They were married on 1 June 1983. Mr
Jackson was working as a motor mechanic in a garage and Mrs Jackson was working locally as a cleaner in a further education college. They were living in a council flat where Mr
Jackson was the official tenant. They have a child, called Norman, who was born in 1987.
After the birth of their son, Mrs Jackson left her job and stayed at home to look after the baby. Recently, Mrs Jackson was subject of cruel behaviour by Mr Jackson and she fled with her child to live with her parents. Mrs Jackson is staying there in overcrowded conditions. In November 1994, Mrs Jackson petitioned for a divorce on the ground that
Mr Jackson has behaved in such a way that she could not reasonably be expected to live with him. Mrs Jackson applied for an order to exclude her husband from the matrimonial home. According to Mrs Jackson, she had been the victim of at least two assaults in May
1993 and July 1993. Because of Mr Jackson’s behaviour, Mrs Jackson was forced to leave home on 3 September 1993. Mrs Jackson stated that she was afraid to go back to their matrimonial home. Mr Jackson denied all his wife’s allegations and said that she had no reason to fear him. However, he offered no adequate explanation for her choosing to leave the matrimonial home and to live in such uncomfortable conditions. There was no evidence from Mr Jackson that he would have any difficulty in finding a suitable alternative
^ Weekly Law Reports, Family Law Reports, All England Law Reports, and Times Law Reports. Chapter 7. Empirical Observations and Validation of ASHSD 162
Case No Case Name Case No Case Name
CASEOl Silverstone v Silverstone CASE26 Akingbehin v Akingbehin
CASE02 Montgomery v Montgomery CASE27 Hopper V Hopper
CASE03 Gurasz V Gurasz CASE28 White V White
CASE04 Tarr v Tarr CASE29 Horner v Horner
CASE05 Jones V Jones CASE30 Pinckney v Pinckney
CASE06 Hall V Hall CASE31 Vaughan v Vaughan
CASE07 Phillips V Phillips CASE32 Shipman v Shipman
CASE08 Brent v Brent CASE33 Carpenter v Carpenter
CASE09 Bassett v Bassett CASE34 Blackstock v Blackstock
CASEIO Walker v Walker CASE35 Scott V Scott
CASEll Elsworth V Elsworth CASE36 Burke v Burke
CASE12 Rennick v Rennick CASE37 Wachtel v Wachtel
CASE13 Mayers v Mayers CASE38 Minton v Minton
CASE14 Samson v Samson CASE39 Moisi V Moisi
CASE15 Hanlon v Hanlon CASE40 Hector v Hector
CASE16 Browne v Pritchard CASE41 Harvey v Harvey
CASE17 Matrin v Martin CASE42 Appleton V Appleton
CASE18 Glutton V Glutton CASE43 Button V Button
CASE19 Goodheld v Goodheld CASE44 Jansen v Jansen
CASE20 Brown v Brown CASE45 Cobb V Cobb
CASE21 Chadwick v Chadwick CASE46 Mesher v Mesher
CASE22 Mortimer v Mortimer CASE47 Bedson v Bedson
CASE23 Pettitt V Pettitt CASE48 Lucas V Lucas
CASE24 Thompson v Thompson CASE49 Regan v Regan
CASE25 Hale V Hale CASE50 Lewis V Lewis
Table 7.1: Real test cases used in the empirical observations Chapter 1. Empirical Observations and Validation of ASHSD 163
C ase No C ase Nam e Case No Case Name
CASE51 Silverstone v Silverstone^ GASE76 Jackson v Jackson
CASE52 Montgomery v Montgomery# GASE77 Grant v Grant
CASE53 Gurasz v Gurasz# GASE78 Underwood v Underwood
CA SE54 Tarr v Tarr# GASE79 Davis V Davis
CASE55 Jones V Jones# GASE80 Dean v Dean
CASE56 Hall V Hall# GASES 1 Buttler V Buttler
CASE57 Phillips V Phillips# GASE82 Mitchell V Mitchell
CASE58 Brent v Brent# GASE83 Pitt V Pitt
CASE59 Bassett v Bassett# GASE84 Willson V Willson
CASE60 Walker v Walker# GASE85 Hobbs V Hobbs
CASE61 Elsworth V Elsworth# GASE86 Dobson V Dobson
CASE62 Rennick v Rennick# GASE87 Nicholson v Nicholson
CASE63 Mayers v Mayers# GASE88 Bush V Bush
CASE64 Samson v Samson# GASE89 Nixon V Nixon
CASE65 Hanlon v Hanlon# GASE90 Turner v Turner
CASE66 Browne v Pritchard# GASE91 Ball V Ball
CASE67 Matrin v M artin# GASE92 Holmes v Holmes
CASE68 Glutton V Glutton# GASE93 Miller v Miller
CASE69 Goodfield v Goodfield# GASE94 Gibson v Gibson
CASE70 Brown v Brown# GASE95 Smith V Smith
CASE71 Chadwick v Chadwick# GASE96 Washerman v Washerman
CASE72 Mortimer v Mortimer# GASE97 MacDonald v MacDonald
CASE73 Pettitt V Pettitt# GASE98 Bull V Bull
CASE74 Thompson v Thompson# GASE99 Smith V Smith
CASE75 Hale V Hale# CASEIOO Baker v Baker
Table 7.2: Derived (hypothetical) test caaes used in the empirical observations Chapter 7. Empirical Observations and Validation of ASHSD 164 accommodation for himself. Mrs Jackson provided evidence of the allegations and these allegations have been corroborated.
A portion of the ASHSD internal representation of the facts for the Jackson v Jack son case is shown below:
(make-instance ‘CASE76
‘CA SE
(quote ($MAT_HOME MATHOME76
$ISSUSES ISSUSES76
$CAUSE_AND_ACTIONS CACTIONS76
SFACTS FACTS76
$PARTICIPANTS_AND_RELATIONS PRELATIONS76
$COURT_NAME “unknown”
$CASE_NAME “Jackson v Jackson”
$CASE_INDEX INDEX76)
))
(make-instance ‘INDEX76
‘IN D EX
(quote ($FIRSTJNDEX FINDEX76
$SECOND_INDEX SINDEX76
$THIRD-INDEX TINDEX76)
))
(make-instance ’FINDEX76
‘FIN D E X
(quote (SFINDEXES ((EXCLUSION ORDER)) ) Chapter 7. Empirical Observations and Validation of ASHSD 165
))
(make-instance ’SINDEX76
‘SIN D EX
(quote ($SINDEXES ((HOME TYPE IS RENTED)
(APPLICANT IS THE WIFE)
(RESPONDENT IS THE HUSBAND)
(APPLICANT IS STAYING IN A SEPARATE ACCOMMODATION)
(RESPONDENT IS STAYING IN THE HOME)
(RESPONDENT HAS USED CRUELTY AGAINST THE APPLICANT)
(DIVORCE PROCEEDINGS PENDING)
(RESPONDENT DENIES THE ALLEGED CRUELTY)
(RESPONDENT IS THE OFFICIAL TENANT)
(EVIDENCE OF THE ALLEGATION IS CORROBORATED)
(DEPENDENT CHILDREN ARE LIVING WITH THE APPLICANT)
(APPEAL FOR INJUNCTION)
))
(make-instance ’TINDEX76
‘TIN D E X
(quote (STINDEXES (SLENGTH.OFJVIARRIAGE SOMEWHAT MIDDLING
$NO_OFJDEPENDENT_CHILD 1
))
In the scoring mechanism, ASHSD converts the above index data structures (FINDEX76,
SINDEX76 and TINDEX76) into a list which contains the important facts. The list of facts for Jackson v Jackson case is as below: Chapter 7. Empirical Observations and Validation of ASHSD 166
‘exclusion order’, ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the hus band’, ‘applicant is staying in a separate accommodation’, ‘respondent is staying in the home ‘respondent has used cruelty against the applicant ’, ‘divorce proceedings pending
‘respondent denies the alleged cruelty’, ‘respondent is the official tenant’, ‘evidence of the allegation is corroborated’, ‘severity of the allegation is dangerous’, ‘dependent children are living with the applicant’, ‘appeal for injunction’, ‘there are dependent children’, and
‘length of the marriage is somewhat middling’.
Using the above facts, if we try to obtain rule based advice for the Jackson v Jackson example, BRULE105 from ASHSD’s rule base is triggered. Its prediction and justifications are shown below:
PREDICTION:
In this case a general eviction order is granted, and the husband will be evicted
from the house within two weeks of the order. It is instructed that he must not
threaten or use violence against the wife.In this case it is also advised that a
power of arrest be granted which would allow the police to arrest the husband
if the order is broken.
JUSTIFICATION:
The husband denied assaulting the wife. However, evidence of this allegation
has been provided and the allegation is corroborated. Therefore, it has been
decided that the husband has to move out of the matrimonial home. Due to
the severity of the assault and the request from the wife, an order for power of
arrest has also been granted.
The BRULE105 with its different categories of preconditions is shown below: Chapter 7. Empirical Observations and Validation of ASHSD 167
(make-instance ’BRULE105
‘BRU LE
(quote (PERIPHERAL ((exclusion order)
(home type is rented)
(applicant is the wife)
(respondent is the husband)
(length of the marriage is somewhat middling)
(divorce proceedings pending))
SIGNIFICANT ((respondent denies the alleged cruelty)
(there are dependent children)
(respondent is the official tenant))
ESSENTIAL ((respondent has used cruelty against the applicant)
(evidence of the allegation is corroborated)
(severity of the allegation is dangerous)
(dependent children are living with the applicant)))
))
In order to find the most relevant cases for the new case, ASHSD uses the facts of the
Jackson v Jackson case. The most suitable three cases retrieved from the ASHSD case base are: CASE04, CASE02 and CASE05.
The first step in the similarity measure is to aggregate the attribute values of F I R S T J —
NDEXid, SECOND JNDEXid^ and extract the attributes $no_of_dependent_children and the $length_of_the_marriage from the THIRD JNDEXid into a flat list structure for the new case. The same technique is applied to retrieve similar cases from the case base. Chapter 7. Empirical Observations and Validation of ASHSD 168
The second step is to produce a flat list from the selected rule’s data structure. Then the similarity coefficients are calculated by using the binary relationship as discussed in section 5.3.1 of chapter 5.
The main features of the retrieved cases are given below:
For example, in Tarr v Tarr (i.e. CASE04), the attribute $secondindexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’, ‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘divorce has been granted’, ‘dependent children are living with the applicant’,
‘couple are living separately in the same home’, ‘appeal for injunction’, ‘exclusion order refused’, ‘question on jurisdiction to exclude the legal tenant’, ‘exclusion of the legal tenant is confirmed by the court of appeal’. The attribute $thirdJndexes of CASE04 refers to the surface features ‘no_of_dependent_children 1’, and ‘length_of.marriage middling’.
Similarly, in Jones v Jones (i.e. CASE05), the attribute $secondJndexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’, ‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘allegation of adultery against the respondent’, ‘applicant left home’, ‘applicant is staying in a separate accommodation’, ‘dependent children are living with the applicant’,
‘divorce proceedings pending’, ‘appeal for injunction’, ‘exclusion order refused’, ‘reappeal for exclusion order’, and ‘reappeal granted’. The attribute $thirdJndexes of CASE05 refers to the surface features ‘no_of_dependent_children 2’, and ‘length.ofjnarriage long’.
Similarly, in M ontgomery v M ontgomery (i.e. CASE02), the attribute $sec- ondJndexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’,
‘respondent is the husband’, ‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘divorce has been granted’, ‘couple are living separately in the same home’, ‘appeal for injunction’, ‘appeal refused’. The attribute $thirdJndexes of CASE02 refers to the surface features ‘no_of_dependent_children 2’, and ‘length.ofmiarriage long’. Chapter 7. Empirical Observations and Validation of ASHSD 169
Applying the same inspection to our test example, Jackson v Jackson (i.e. CASE76), we find that the attribute $secondJndexes refers to the surface features ‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’, ‘applicant is staying in a separate accommodation’, ‘respondent is staying in the home’, ‘respondent has used cru elty against the applicant’, ‘divorce proceedings pending’, ‘respondent denies the alleged cruelty’, ‘respondent is the official tenant’, ‘evidence of the allegation is corroborated’,
‘severity of the allegation is dangerous’, ‘dependent children are living with the applicant’, and ‘appeal for injunction’. The attribute $third_indexes of CASE76 refers to the surface features ‘no_of_dependent_children 1’, and ‘length_of_marriage middling’.
Let us give the names RLIST105, CLIST04, CLIST05, CLIST02 and CLIST76 to the flat lists for BRULE105, CASE04, CASE05, CASE02 and CASE76 respectively. The con tents of RLIST105 are ‘exclusion order’, ‘home type is rented’, ‘applicant is the wife’,
‘respondent is the husband’, ‘length of the marriage is somewhat middling’, ‘divorce pro ceedings pending’, ‘respondent denies the alleged cruelty’, ‘there are dependent children’,
‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘evi dence of the allegation is corroborated’, ‘severity of the allegation is dangerous’, ‘dependent children are living with the applicant’. The contents of CLIST04 are ‘exclusion order’,
‘home type is rented’, ‘applicant is the wife’, ‘respondent is the husband’, ‘respondent is the official tenant’, ‘respondent has used cruelty against the applicant’, ‘separation order has been granted’, ‘dependent children are living with the applicant’, ‘couple are living separately in the same home’, ‘appeal for injunction’, ‘exclusion order refused’, ‘question on jurisdiction to exclude the legal tenant’, ‘exclusion of the legal tenant is affirmed by the court of appeal’, ‘there are dependent children’, and ‘length of the marriage is mid dling’. Similarly, the contents of CLÎST76 are ‘exclusion order’, ‘home type is rented’,
‘applicant is the wife’, ‘respondent is the husband’, ‘applicant is staying in a separate ac commodation’, ‘respondent is staying in the home’, ‘respondent has used cruelty against the applicant’, ‘divorce proceedings pending’, ‘respondent denies the alleged cruelty’, ‘re spondent is the official tenant’, ‘evidence of the allegation is corroborated’, ‘severity of the allegation is dangerous’, ‘dependent children are living with the applicant’, ‘appeal for injunction’, ‘there are dependent children’, and ‘length of the marriage is somewhat m iddling’. Chapter 7. Empirical Observations and Validation of ASHSD 170
Test Case No Retrieved Rule No Retrieved Case No Rule Score Case Score Decision Category
CASE06# BRULE108 CASE06 0.7417 0.8976 R-C++ BRULE 122 CASE09 0.6190 0.7595 R-C+ BRULE 106 C A SE ll 0.6190 0.6904 R-C+
CASEIO# BRULE 122 CASEIO 0.5104 0.8492 R-C++ BRULE 108 CASE08 0.4879 0.6458 R-C+ BRULE 125 CASE 12 0.4375 0.5833 R-C+
CAS El 2# BRULE 122 CASE12 0.6098 0.8712 R-C++ BRULE108 CASE03 0.6713 0.7090 R-C+ BRULE125 CASE06 0.5227 0.6303 R-C+
CASE 13 BRULE105 CASE 13 0.4500 1.0000 R-C++ BRULE 102 CASE06 0.4500 0.6000 R-C+ BRULE121 CASE09 0.4307 0.6000 R-C+
CASE19 BRULE24 CASE 19 0.5333 1.0000 R-C++ BRULE 12 CASE 18 0.5523 0.6111 R-C+ BRULE22 CASE16 0.4667 0.5368 R-C+
CASE76 BRULE105 CASE04 0.8750 0.5812 R++C+ CASE02 0.8750 0.5833 R++C+ CASE05 0.8750 0.6066 R++C+
CASE80 BRULE04 CASE22 0.9285 0.3463 R++C- CASE23 0.9285 0.5238 R++C+ CASE21 0.9285 0.3928 R++C-
CASE82 BRULE121 CASE04 0.9333 0.6667 R++C+ CASE02 0.9333 0.6000 R++C+ CASE07 0.9333 0.5333 R++C+
CASE92 BRULE 11 CASE22 0.9411 0.4492 R++C- CASE23 0.9411 0.4975 R++C- CASE21 0.9411 0.3554 R++C-
CASE93 BRULE22 CASE22 0.8395 0.6136 R+C+ BRULE21 CASE 19 0.7104 0.6458 R+C+ BRULE20 CASE17 0.6696 0.5812 R+C+
Table 7.3: The scores and the appropriateness of categories for some test cases Chapter 7. Empirical Observations and Validation of ASHSD 171
The simplest of all association measures is RLIST105 fl CLIST76 which produces 13 shared elements. Taking into account the size of these flat lists, the mutual similarity-
coefficient, psim{RLISTlQb,CLIST76)^ is calculated as 0.8750. Similarly, the mutual similarity coefficient of PsimiCLIST04^CLIST76) is 0.5812 . In this research, the suit
abilities of retrieved cases and rules are judged by examining the original case reports and
related decisions and advice. Eight categories are identified in establishing the suitability of any Ccise or any rule with respect to the new case. They are shown below to explain
the notion in the last column of Table 7.3.
C-1—hR" CBR dominant
CH—f-R-T CBR dominant and RBR can provide some help
towards the advice
RH—hC- RBR dominant
R d —h C - f RBR dominant and CBR can provide some help
towards the advice
R + C - RBR can provide some help towards the advice
R-C- Neither RBR or CBR can help towards the advice
R-C-h CBR can provide some help towards the advice
R+C-f- Both RBR and CBR can provide some help towards the advice
After examining the triggered rule’s (BRULE105) advice and decision for the retrieved
case (e.g. CASE04) in the light of the constructed case CASE76, we have categorised
these items of advice as R-1-+ and C-f.
The same method has been applied for all other test cases. The scores and categories
are collected in a tabular form. A part of the test results is shown in Table 7.3. The
scores indicating how well individual test cases match the closest items in the stock of
knowledge (i.e. with respect to the rule base and the case base) are represented as points
in the two-dimensional plot in Figure 7.1 . The X-axis of this graph refers to the rule-score
{Rscore) and the Y-axis refers the case-score {Cscore)- From the pattern of this graph a
general spread of Rscore^ Cscore and their connections emerges. Chapter 7. Empirical Observations and Validation of ASHSD 172
1.0 IË Ml: (D® ©i a ^ B] IE A ® ®! .A...... ! o o o . Q c O o O o o 0 1 O o □ ...... Jl oO o 1 o o o o o an ] OO 1 1 a a 0°nC nOc? ^ o ...' a a aa o ° o a o o = ) o o : C _^^0.5 a a • • □ □ □ E ^ # • T E • [] r .g .....
□ □ [ ] . / 1 Kl • • □ """O'" - M □ E • • • •
0.2 0.5 1.0
LEGEND
□ =R-C- O =R-C+ A =R+C++ O =R++C+ Q ) = Not covered ^ = R+C- 0 0 = R+C+ 0 = R++C- 1 ^ = R-C++
Figure 7.1: The comparison of RBR and CBR effectiveness on test cases
For each test case, ASHSD has calculated the pair of scores for the best rule and the
best case {Rscore^ Cscore) • We have also examined the case from the legal point of view, to see (independently of the computation) what relative degrees of conviction the rule and case advice carried. We have mentioned such an examination just above, for CASE76, with the result R++C+. After close inspection, we were able to partition the plot into
nine regions as shown in Figure 7.2. Chapter 7. Empirical Observations and Validation of ASHSD 173
©
Figure 7.2: The different decision categories in graphical form
For a particular case, if the point specified by the pair of scores lies within the A area, then neither RBR nor CBR is suitable. When the point lies within the region B, there is a possibility of getting some useful partial advice from the rule base. When a point lies within the region C, the case is suitable for RBR only. If the point belongs to G, then it means that the case is RBR-dominant but at the same time CBR can provide some secondary help towards the advice. When a point lies in the region E, then CBR can provide some help towards reaching an appropriate solution.
In the case of region F, both the RBR and CBR information can provide some (but not conclusive) help towards finding the solution for a new case. However, when a point is in Chapter 7. Empirical Observations and Validation of ASHSD 174 the region H, i.e. close to a match with some existing case(s), the advice is unsurprisingly much more attuned to these cases then anything that any imperfectly-matching rules can say. In the region I, the advice remains CBR-dominant but RBR (which receives a higher
rating than in H) can provide supplementary information which is likely to be of some interest in finding the best advice.
In our experiment we could not invent any legally convincing test examples in the
region J. This is also not surprising, because it would indicate a common-law situation
where, on the one hand, the interpretation would be almost standard in terms of the legal
rules, while on the other hand it would be nonstandard enough to resemble closely a non standard situation covered by one or more cases (and thus not viewed by legal authorities
as suitable for treatment via the rules).
The implemented system analyses a case automatically (if the user asks for the option
that means ‘find the best result regardless of whether it depends on rules or cases’) in
order to decide the position that it occupies on Figure 7.2 . According to whatever this
position is, ASHSD gives appropriate advice to the user.
Typical relationships between the rule-score (Rscore) and case-score (Cscore) for dif
ferent areas of the two-dimensional plot are shown in table 7.3 . We would not expect
any precise relation between rule-scores and case-scores. However, a crude linear fit to
Figure 7.1 would give a line expressing the general tendency that we would expect (i.e.
case-scores go up as rule-scores go down, and the boundary values for either score when
the other score is 1 are sensible). Moreover, the fact that parts of the whole plot in Figure
7.1 are unoccupied is a further indication that the approach is valid, while a substantial
spread over the plot would have implied the opposite.
7.3 Conclusions
A scoring mechanism which helps to determine the suitability of RBR and CBR methods in
matrimonial home settlement cases is described. It has been shown that such a mechanism
enhances the applicability of automated reasoning methods. The legal area has been Chapter 7. Empirical Observations and Validation of ASHSD 175 selected for the development of the prototype system, ASHSD. This scoring mechanism may be applicable in other domains where rules and Ccises are used (Pal & Campbell,
1996b).
The development of the ASHSD system is also a clear demonstration of the suitability of personal computers for KBS tasks. During the prototype stage, the system comprised
more than 190 rules and 50 manually-coded cases.
The incremental approach in the development was a major contributory factor in the success of the project. It enabled the researcher to review the development of the system
at every stage. By structuring the system and dividing it up into separate knowledge
bases, with no more than 65 rules in any one, problems associated with large individual
knowledge bases, such as excessively laborious testing and amending, were avoided.
7.4 Overview of the Project Validation
Knowledge-based systems incorporate human reasoning in automated ways to perform
tasks normally carried out by experts. The ability of an automated reasoning system
to arrive at a ‘correct decision’ is an important consideration. An incorrect system may
make costly errors, or may not perform up to expectations. In either situation, the decision
generated by the system may be inappropriate or wrong. Some validation is thus a very
important requirement for any knowledge-based system that is intended for serious post
development use. A suitable definition of the validation process (Adrion et al., 1982) is
‘validation is the determination of the correctness of the final program or software produced from a development project with respect to the user needs and requirements’. We have tried
to follow this description in the project that is reported below.
7.4.1 Validation of ASHSD
The ASHSD knowledge base consists of a rule base and a case base. In our first step we
have inspected these sub-parts of the knowledge base for validity separately. Chapter 1. Empirical Observations and Validation of ASHSD 176
Rule base Validation
A wide range of techniques is available for the validation of rule-based systems. An overview of a number of techniques is given in Lopez (Lopez et ah, 1990). In our research we have followed a two-step validation process for the overall correctness of the rule base.
The first step is to test the rule base for its consistency by the following method. We submit the preconditions of each rule of the ASHSD rule base as a new legal problem to the system and check whether more than one rule is triggered or not. Multiple rule- triggering that produces different conclusions results in inconsistency. For example, if we have three rules:
R o i = {PCroi, PCr02, PCr03,conclusionoi}
R q2 — { R ^ r O l ^ PC'r02') POr03^ COTlcl'lLSiOTlQ2}
R q3 = {PCroi, PCr03,conclusiono3} ,
the knowledge base(KB) will be inconsistent for the fact set (PCroi, P C r 02 i PC'roa) when conclusionoi and conclusiono^ are different in any significant legal sense.
We should note that this does not amount to a complete coverage of the sorts of issue that need to be checked; O’Leary (O’Leary, 1991) gives a survey of the frameworks and various case studies that have been performed. This is a large subject area and it is beyond the scope of this thesis.
The next move in validation of the rule base is to examine the advice and justifications that it provides with the help of a legal expert. We return to this in section 7.4.2.
Case base Validation
For the case component of ASHSD, the initial inspection was by a legal expert from the
Faculty of Laws, University College London. The expert was knowledgeable in English divorce law. The validation was performed by a combination of discussions and comments from the legal expert about the validation experiment which is described below:
We have selected 17 cases from the ASHSD case base as an input case set T (the Chapter 7. Empirical Observations and Validation of ASHSD 177
member test cases are (i, (2 , ^3 , for the validation process. For each test case
tn, ASHSD retrieves the four most similar cases to tn- We then focus on the three cases
that are left when in is removed. These cases fall into three categories, namely ni, ri2
and ?%3 . Category n i includes cases that are cited by the judges, and applied to make
the final decision. Category Mg includes cases that are cited but only distinguished by the judges. Finally, category M 3 presents cases that are cited just by the lawyers for presenting
arguments, but not mentioned by the judges. Legally, these three categories have different
weights as far as significance is concerned. It is reasonable to express the significance
in terms of assigning a weighting factor (e.g. where i = 1,2,3), where the ordering
of the significances requires that Wi > W2 > W3 . After examining the case reports and
consulting the legal expert, we have chosen values of w i = 0.9, wg = 0.7 and ws = 0.5.
Since (mi + ng + M 3 ) < 3, the best possible weighted rating of a case in our method of case
retrieval is 3u;i. We can define a validation index (VI) as :
y r (Wini+W2n2+W3n3) ^ ^ — 3wi
When VI is 0, it indicates that the case base has a complete absence of useful infor
mation or a gap in part of the relevant knowledge. On the other hand, when VI is 1 , it
shows an excellent coverage there,
A further kind of validation of ASHSD’s case base comes from the area of information
retrieval, where a '‘recall factor'’ (RF) is a standard index. Let N be the total number of
relevant cited cases stored in the case base with respect to a particular input case, and
RC be the number of relevant cases retrieved from the case base. In other words, RF is
equivalent to the proportion of relevant cases actually retrieved in response to a search
request. The recall fa cto r is then defined as:
The results, in terms of VI and RF, for the 17 cases are shown in Table 7,4, The legal
expert has defined a grading structure to assess the results and Table 7.5 sets out the
criteria of assessment: Chapter 7. Empirical Observations and Validation of ASHSD 178
The values of VI lie between 0.26 and 0.78, apart from the unusal CASEll, which we discuss later, where it is 0. The average value of the validity index for the above test cases is 0.50. During the validation process, we have consulted our legal expert about the relevance of each retrieved case and on the basis of his observations we have calculated the
recall factor RF for test cases. Table 7.4 shows the result that ASHSD always retrieves relevant cases during the test: the RF values for all the test cases are 1. We would certainly not expect this level of performance in a larger test set, but it shows that the current version of ASHSD and its case knowledge is highly efficient according to the ‘recall’ criterion.
Now let us consider reasons for the least satisfactory values of VI in the tests. It happens that the case Akingbehin v Akingbehin^ was applied in the final judgement of CASE06. But the length of marriage in the Akingbehin v Akingbehin case report was missing. As a result, as that was one of the cues for similarity, ASHSD failed to retrieve the Akingbehin v Akingbehin case, which affected the value of VI for CASE06.
This was a defect of the raw data and not of ASHSD.
As another example, no cited cases in the report for CASE09 were used as components of the actual judgement. All the cases cited in that particular situation were only for the
purpose of comparison and distinction. Further, in CASEIO, only one case was both cited and applied in the judge’s decision. ASHSD retrieved that cited case sucessfully, but this situation produces an unusually low value of VI while not reflecting on any deficiency of
ASHSD. In yet another test, ASHSD retrieved three similar cases for CASE17, of which the retrieved case Hector v Hector^ was not in the cited list. The other two retrieved cases were just in the list of cases referenced by the lawyers. The value of VI was therefore low,
and we can say that this low rating here shows up a deficiency in ASHSD’s knowledge
base. In the test on CASE19, ASHSD retrieved three cases, but only one of these three was applied in the final judgement. The other two retrieved cases were not in the cited list of cases. This kind of situation also affects the value of VI adversely. The same argument is true for CASE20. For CASE22, only one case was referred to in the judgement and there
^Akingbehin v Akingbehin [1964] 108 S.J. 520, CA
^Hector v Hector [1973] 3 All ER 1070 Chapter 7. Empirical Observations and Validation of ASHSD 179
Case No Validation-index (VI) Recall-factor (RF)
CASE03 0.67 1
CASE05 0.78 1
CASE06 0.51 1
CASE07 0.59 1
CASE08 0.59 1
CASE09 0.52 1
CASEIO 0.33 1
CASE) 1 0 1
CASE13 0.59 1
CASE14 0.59 1
CASE 15 0.59 1
CASE 17 0.51 I
CASE] 8 0.59 1
CASE! 9 0.33 1
CASE20 0.33 1
CASE22 0.26 1
CASE23 0.67 1
Table 7.4: The results of the case base validation
were no other cases cited in the case report. ASHSD retrieved the cited case successfully, but ultimately the value of VI was low because this index makes no allowance for situations where the number of cases quoted in a case report is very small.
It was not feasible to try all cases in our case base as members of T for similar tests during this validation process. The main reasons were:
[a] No cited cases in the case reports. For example, CASE04, CASE12 and CASE21 fall
into this category.
[b] In other instances there was incomplete information in the cited case reports (e.g. un
known length o f marriage^ unknow n number of children, etc.). CASEOl and CASE02
fall into this category. Chapter 1. Empirical Observations and Validation of ASHSD 180
VI / R F G rade
0 .8 5 - 1 good
0.50 - 0.84 average
0 - 0.49 poor
Table 7.5: Grading structure to assess the ceise base validation
[c] There were a few cited cases in some case reports for arguments only, but the judges
did not consider these cases in their decision-making process. CASEll, CASE24
and CASE25 are of this type.
For CASEll, only one case was cited in the case report (and in fact was only of type
ns). ASHSD failed to retrieved that particular case; hence VI for CASEll was zero.
This is the worst ‘criticism’ of ASHSD in the tests, but the criticism is not necessarily strong. For example, it could be said that ASHSD’s ‘lawyer’ in CASEll had an honest
disagreement with the opposing lawyer who cited the one case in support of his argument,
and that the judge sided with ASHSD.
The legal expert indicated his support for the VI scoring mechanism during the valida
tion session. In doing this, he took into account all the adverse effects on the average value
of VI that the most peculiar test cases (CASE06, CASE09, CASEIO, CASEll, CASE17,
CASE19, CASE20 and CASE22) caused. We have explained the nature of the effects
above.
ASHSD is a prototype system, and its quality of performance (VI and RF values)
should be improvable by adding more cases to the case base. Nevertheless, the results of
this experiment according to the expert and his expectations as expressed in Table 7.4 lie
between ‘average’ a,nd ‘good’.
The next step of the validation process was to submit the whole system and its be
haviour for review by the legal expert. Chapter 7. Empirical Observations and Validation of ASHSD 181
7.4.2 Second Step of Validation
In this step, validation was performed by a combination of discussions and questionnaires.
The model was described at length and the software was demonstrated to the legal ex pert. Subsequent discussions lasted for about 45-50 minutes in each session. Notes were taken during each discussion. Appendix B contains the questionnaire used in the valida tion process. The expert examined the model and provided comments on the following categories:
1. Measurability: The expert expressed agreement that such a hybrid model of knowl
edge might be useful in a wide range of domains including intellectual property,
patents, royalties, trademarks and others. At this time, he saw the model as poten
tially useful for legal areas that are well established as opposed to areas in which
the law is changing rapidly. He also observed that updating such a knowledge base
might be a problem if rapid changes were occurring. If the rate of change in law
were not so frequent, then the particular model used might prove to be attractive.
2. Realism: The expert responded positively on this front and observed that it is incor
rect to assume that real legal decisions are very mechanical and therefore amenable
to a decision tree or some logic-based approach that is simpler than the one we have
used.
3. Usability: It was observed that our model could be useful for several reasons:
• To provide decision support;
• To act as a checklist for decision-makers, confirming that nothing has been left
out from the decision-making process;
• To provide a better understanding of the decision and formulation of the prob
lem;
• To assist law students in understanding legal reasoning;
4. Discussion: The expert particularly liked some of the features of the hybrid ap
proach - the modularity, the creation of partial solutions, and the use of the hybrid
architecture as a medium for interaction. Chapter 1. Empirical Observations and Validation of ASHSD 182
C riteria Assessm ent
Model good
Usability good
Quality of advice good
Table 7.6: Validation of ASHSD by the legal expert
The expert observed that the traditional pure rule-based approach led to a mechani
cal process and was more appropriate for tasks such as tax analysis, estate planning,
pension-fund laws, and for will creation, etc. However, he saw the hybrid model as
being more appropriate for ‘matrimonial home settlement after divorce’ cases. The
discussion ended by talking about the fact that law cannot be made scientific by elim
inating emotion and other related factors with the use of mechanical decision-making
systems. While ASHSD cannot compute with such factors, at least its tentative for
mat for offering advice in sessions with users does not give the impression that the
outputs are of Hegal orac/e ’ quality. The user is therefore impelled towards further
thought on how the cases that it has processed will actually be decided.
During the validation process, the expert did not suggest any major modifications
to the model. The model, in his view, reflected reality.
Table 7.6 summarises the results of the validation by the expert. The assessment good indicates that the model was evaluated positively.
7.5 Knowledge Base Maintenance Aspects of ASHSD
In the development of any legal knowledge-based system, one should specifically keep in mind the changing character of law. As a result, the maintenance should receive proper attention at the time of development of knowledge-based systems. This has already been pointed out by various researchers [e.g. (Bench-Capon & Coenen, 1991c), (Bench-Capon
Sz Coenen, 1991a), (Bench-Capon & Coenen, 1992), (Bratley et al., 1991), (Weusten,
1989)] in various contexts. Bench-Capon and Coenen phrase the importance as below: Chapter 7. Empirical Observations and Validation of ASHSD 183
It is our belief that the greatest barrier to the routine use of KBS techniques
for practical legal applications lies not so much in the problem of building the
systems, since this process is becoming better understood, but in the problems
associated with the maintenance of such systems. For no one is going to invest
the amount of effort involved in building a legal KBS unless he can have some
assurance that the system will have a reasonable length of useful life. And since
one certain thing is that the law will change over time, this means that there
must be a clear strategy to enable the system to cope with these [(Bench-
Capon & Coenen, 1991c), p.6]
The system structure of ASHSD is conditioned partly by consideration of maintenance
- in particular for coping with changes during knowledge base development (a lesson men tioned routinely in textbooks [e.g. (Coenen & Bench-Capon, 1993)] on the building of knowledge-based systems), in response to considerations like this.
Keeping textbook ideas in mind, the legal knowledge for ASHSD was collected and structured in such a way that the maintenance would be easy. This was ensured by drafting structure charts as shown in chapter 3. These were eventually transformed into decision trees in order to check the one-to-one relationship between analysis and representation at a later stage. As mentioned before, the knowledge base of ASHSD keeps the domain knowledge in two different forms:-
1. Rule form : I F
2. Case form : Representation of previously-decided case reports in a nested data struc
ture, known as hypernode.
It was found that the knowledge we had to process had a natural subdivision that allowed us to partition the rule base into three parts, namely RULE-TYPEOl, RULE>TYPE02 and RULE-TYPE03. RULE-TYPEOl refers to the owned matrimonial home settlement problems, RULE-TYPE02 handles tenancy transfer problems, and RULE-TYPE03 covers the set of rules necessary to determine severity of injunction. All these rules fall into two main types: type one for giving 'available-action(s) ' and the type two for presenting Chapter 7. Empirical Observations and Validation of ASHSD 184
‘prediction’ for a particular case. The rules of the rule base are further structured according to the outline that we have given in chapter 3 and chapter 6. Similarly ASHSD’s case base was partitioned into three parts: CASE-TYPEOl, CASE-TYPE02, and CASE-TYPE03.
All the owned matrimonial home settlement cases are stored in CASE-TYPEOl. Similarly,
CASE-TYPE02 and CASE-TYPE03 store cases related to tenancy transfer and severity
of injunction respectively. Each case was given a unique identification (e.g. CASEOl,
CASE02, CASE03, etc.). This first level of structure helps any maintainer of ASHSD
to focus quickly on the possibly relevant knowledge for such modifications and to avoid
irrelevant searches. Moreover, it ensures that new cases are added to the case base in
the correct place as and when these become available. ASHSD provides straightforward
maintenance facilities for both rule bases and case bases. We hold two separate association
tables for the knowledge base: one for the rule base and the other for the case base. These
are the repository for metaknowledge, i.e. knowledge about the stored knowledge.
As we mentioned before, the rule-base consists of two types of rules: available-action(s)
rules, and prediction rules. All these rules are made up of one or more preconditions. The
available-action (s) rules were straightforward transformations of statute to IF
T H E N
formations of statute material to rule. The preconditions of prediction rules were derived
by interpreting textual information which is made up of sections/subsections of statues.
Each and every rule was given a unique name. The rule-base association table keeps infor
mation about all rules. It contains not only the rule names (e.g. ARULEOl, ARULE02,
..., BRULEOl, BRULE02, .... etc.), but also the application domains (e.g. owned home
settlement, tenancy transfer and severity of injunction), tags , and the last date of mainte
nance. The contents of a tag reflect the section and subsection identity of the statutes used
to craft a particular rule. When a statue or a section/subsection of a statue is amended,
we can thus easily identify the associated rules in the table that are affected. Once the
rules are identified, the constituent preconditions can be modified accordingly. Similarly,
the association table for the case base keeps information about all stored cases. This in
formation includes case name (e.g. CASEOl, CASE02, etc.), section/subsection of statues
that are overruled (or applied) and the last date of modification of the case base. Hence,
when a particular section/subsection of a statue is amended or revoked, we can identify Chapter 7. Empirical Observations and Validation of A5H5D 185 the associated cases in the table that are affected. The maintenance facility works well with the present protype system; whenever we have inserted an additional case during our scans through the literature, we have in effect been making a small (successful) test of this claim. Chapter 8
Conclusions
8.1 Introduction
This final chapter is a place to sum up the research that has been reported, consider its ramifications and limitations, and look at directions for future research. In the previous chapters, there is a rather linear narrative, where each major point leads on to the next.
However, the research did not develop like that. The ideas do not ‘really’ go in such a simple linear order from beginning to end. They have arisen as part of a complex whole in which motivations, intuitions, and research form a connected web. Having now seen the various parts presented, the reader is in a position to escape from the linearity of the chapter structure that has reported the work so far.
8.2 Motivation
The most important motivation for this research came from the researcher’s personal expe rience with real-world information processing environments and observation of a distinct lack of automated tools for any types of legal professionals. While at the same time com mitted to the concept of AI as an intellectual tool to aid problem solvers, the researcher also had strong misgivings about the form of some knowledge-based system research which
186 Chapter 8. Conclusions 187 argued that legal knowledge-based systems could be constructed just out of collections of
‘rules’ (e.g. legal rules). As has been argued in this thesis, it is more realistic to believe that a knowledge-based system that attempts to encapsulate merely rules of a domain of legal inquiry is of limited use as a décision-support tool. Such a automated tool would be one person’s theory of the law in a domain. One might imagine going to some single well-known legal professional in English divorce law and constructing a knowledge-based system containing his or her personal version of that area of law. A family lawyer in private practice might appreciate having access to such an encapsulation of the finest thinking on family law, but he or she would not necessarily accept the ‘answers’ provided by such a system. The family lawyer has the goal of maximisation of the client’s interest - which inevitably guides an interpretation of law in his or her own special and interest-dependent way. But the history of litigation in English divorce law - as in most areas of law - shows that the interpretations of lawyers are not always accepted by judges.
What is needed, then, is a knowledge-based décision-support system that respects relevant information expressed in rules but that also comes closer to the task that confronts the lawyer: the analysis of clients’ cases in terms of previously-decided case reports. Hence the motivation to study rule-based reasoning and case-based reasoning in an integrated environment which can support plausible decision-making for a new case.
8.3 Summary of Results
ASHSD is a hybrid legal decision-making system which uses a combination of RBR and
CBR for matrimonial home settlement issues after divorce. It demonstrates a solution to the research problem by providing a hybrid reasoning model in the English divorce law domain. The user inputs a case description to ASHSD and can select either reasoning method (i.e. RBR or CBR) from the system menu or indicate no preference. In case of se lection of the RBR option, ASHSD is capable of providing three types of rule-based advice: comprehensive advice, partial advice, and no advice. In comprehensive advice, the system provides the possible avail able-action (s) plus a prediction of a court’s decision, provided that at least one of the prediction rules has triggered. The partial rule-based advice can Chapter 8. Conclusions 188 be in one of two categories. For category-one of partial rule-based advice, ASHSD offers the relevant available-action(s) and also presents a speculation (in the sense in which we have defined that term). The category-two of partial rule-based advice produces no pre diction or speculation but does suggest some valid available-action (s). Finally, the system provides no rule-based advice at all when it fails to come up with available-action(s) or any kind of predictive information. The system also exploits features of previously-decided case reports, if the user wishes to focus only on cases, to choose similar cases from a rel evant case base and display their details and decisions. When no preference is indicated, the system applies each method of reasoning separately, and then presents results based on an automated relative rating of the qualities of the RBR and CBR advice, as discussed in the previous chapter.
ASHSD addresses four component problems of creating a hybrid reasoning model:
• How can one generate ‘partial’ rule-based advice when none of the rules from the
rule-base has triggered ?
• How is the case-report information represented and organised ?
• How are the most relevant previously-decided cases selected ?
• How does one provide some answer to the question of whether a particular case is
suitable for RBR or CBR ?
ASHSD represents previously-decided case-report information as specific facts and as abstract data structures known as ‘hypernodes’. The facts are represented in a nested case representation approach that allows enough flexibility to record the wide variety of situations that can occur in English divorce cases. ASHSD uses a three-step algorithm to identify the most similar cases to a given problem, from the case base. This three-step algorithm helps to cut down the number of cases selected in the steps and thus improve the efficiency of such computations.
ASHSD is intended to have some useful connections to the following three aspects of human reasoning processes in matrimonial home settlement cases: Chapter 8. Conclusions 189
1. Rule-guided decision-making;
2. Similarity assessment to select the most suitable cases for a new case; and
3. Ability to generate effective information from an existing stock of knowledge.
The rule-guided decision-making of humans is reflected in ASHSD’s rule-based rea soning process. We have chosen an area of law that largely escapes the philosophical difficulties discussed in chapter 1, so that the actual use of the rules themselves is the same for the computation as for a human user. But the choices of whether to use the rules, which involves giving information that establishes the truth or otherwise of precon ditions, and the decision on whether the proposed advice is to taken up or not, are entirely for the user.
ASHSD also offers a way to use previously-decided cases as the basis for resolving a new case. For a given new case, ASHSD retrieves several similar cases from the case base.
These retrieved cases provide the basis for the advice. The solutions the system produces are again subject to review by the user. We do not claim that a human legal reasoner who deals with cases goes immediately to a similarity-metric approach such as ours, but in the last resort, the human choices amount to the imposition of some metric on the space of possibilities, as any statement of the form “A is more similar than B is to C” is saying that d(A,C) < d(B,C) for some metric(s) d. We have not tried to uncover further details of how legal specialists behave in identifying similarities, simply because there are no adequate published accounts that deal with this at a level that would constitute good evidence for a researcher in experimental psychology or cognitive science.
The production of partial rule-based advice and the determination of suitability of reasoning methods reflect the human ability to generate effective information from an existing stock of knowledge. Finally, the system’s justification facilities show that ASHSD can present an apparently reasonable explanation of its decision-making process, which is an important aspect of support for human decision-making. If ASHSD does not always arrive at the same answer as judges, the system will be no different in that respect from good students or legal practitioners. We should remember that ASHSD is a décision- support system and not a decision-making system. Chapter 8. Conclusions 190
8.4 Critical Inspection of ASHSD
The critical inspection was performed by using a set of test data from cases other than those already in the case base. ASHSD’s predictions and how they related to the known decisions in those cases were examined by the researcher, six undergraduate law students who were well aware of matrimonial-home-related English legislation in divorce, and two legal experts. One of these experts was Dr Stephen Guest who was the main domain expert of the project. The other legal expert was Dr Dan Hunter, visiting Cambridge University on leave from Australia, whose area of research is also AI and Law. On the basis of the test runs, both of the experts were convinced by the quality (adequacy, avoidance of legal mistakes, usability) of information retrieved from ASHSD’s rule base and case base. In particular, under the ‘advice quality’ heading in the questionnaire in Appendix
C, the ‘good’ response was an expert response and not just an alternative selected by the students. However, they commented on the different aspects of ASHSD, and our suggestions for future extension reflect their criticism.
The inspection of ASHSD by the students were performed individually, with a com bination of discussions and questionnaires. The architecture of the system was described at length and the implemented system was tested by the students using sample test cases which were collected from different family law reports but were not presented in ASHSD’s case base. Each such session lasted for between 60 and 70 minutes. Appendix C contains the full questionnaire used in this process, which focused on the ‘usability’ aspect m en tioned above. Assessment of the quality of the legal information was left primarily to the experts, but the ‘poor’ options in the questionnaire allowed the students the chance to comment if they had perceived inadequacies in that direction.The students were comfort able in using the system on their own and they expressed positive willingness to use this type of décision-support system. They also commented on different aspects of ASHSD’s present style of operation. On the basis of the examination, the following conclusions were draw n:
• ASHSD does not capture the details of professional legal practitioners’ problem
solving behaviour. The outputs and the computational details are not designed to Chapter 8. Conclusions 191
imitate this behaviour. But the outputs act as a kind of support for less expert users,
which is clearly relevant in assisting a final decision. In particular, in the family-law
decision-making process, legal practitioners use both legislative rules derived from
legislation and previously-decided case reports. The current version of ASHSD is
capable of delivering relevant information of both types, with useful estimates of
relative importance of different items.
• English family-law-related decision-makers are entitled to exercise judicial discretion.
It was realised that legal areas involving largely unfettered discretion are difficult
to model. For example, the decision-makers of the Family Court can only make a
personal protection order if they are satisfied that the respondent has used violence
or threatened to use violence against the applicant or a family member in the custody
of the applicant. But no guidance is given in the Act as to how the decision-makers
should resolve that point. Thus, the decision-makers have a wide discretionary
power. They have less discretion with regard to the other two areas of tenancy
transfer and owned-home settlement cases in divorce, and the rules of ASHSD are
observed to come closer to expressing their reasoning in these areas, as well as initial
decisions about whether certain facts that are subject to interpretation (e.g. about
domestic violence) are indeed true for the current case.
The general lesson is that it is unlikely that an automated system can replicate legal
thinking processes closely, because computerised décision-support systems at present
seem inconsistent with notions of non-mechanistic advice. However, ASHSD handles
the issue of advice by giving information but leaving the final decision to its user.
This prototype system is therefore consistent with a basically discretionary view of
interpretation, and does not intrude into aspects of decision-making where the state
of the art in AI is still not ‘humanistic’ enough.
• Currently there is no facility to record the consultation process. However, a note
book style format could be kept to record the consultation process. This notebook
could then be used as a secondary resource for future consultation as well as for the
refinement of the existing knowledge base. The contents of a case in notebook for
mat should include consultation history, advice, and suitability of given advice. The
present version of ASHSD fails to keep track of any of these kinds of information. Chapter 8. Conclusions 192
This is a significant negative aspect of ASHSD as far as some users are concerned.
• ASHSD’s rules were crafted by the researcher with the help of statutes, text-books,
assistance from undergraduate law students, and guidance from legal experts. Inter
pretation of the statue and related description has helped the researcher to design
the pre-conditions of these rules. Each rule has been constructed from one or more
statutes. But currently, when a piece of advice is given, related statutes are not
shown alongside the preconditions. If the above facility were added, then the user
would be able to compare the preconditions with appropriate statutes and cross
check the current knowledge in ASHSD. This was another possible improvement,
from certain users’ points of view.
8.5 Limitation and Future Research
It is not simple and probably not very useful to distinguish the limitations of the research described here from the need and promise of future research. The limitations naturally suggest the need and desirability for additional research. We cover these under four sub headings below.
8.5.1 Integration of ASHSD with a Data Base Management System
ASHSD is a prototype research system. At present 50 cases are stored in its case base. The reduction of those natural-language reports to the notation of ASHSD’s representation has been performed by hand. The system is implemented in a portable dialect of the LISP programming language. The case selection process with regard to computer search time could be improved by using a more powerful machine and a relational or object-orented database. Database systems are built (usually) for efficient performance. To utilise them, we would need to find the best translation of our current representation into acceptable inputs to databases. Chapter 8. Conclusions 193
8.5.2 Incorporation of a Better User Interface
ASHSD uses a menu-driven and text-based user inteface. It collects facts for a new case in
a question-answering session with its user. A user types the main surface features of a new case, following prompts. However, if a graphical user interface (GUI) were used instead of
the current text-based interface, then the probability of a user making typographical errors
would be reduced. In addition, the system would be easier to use. Also, if the interface
could be made flexible enough to permit a user to exercise initiative rather than being
always subject to ‘control’ by menus - particularly the sort of intelligent user who can
be presumed to exist in legal applications - ASHSD should become a qualitatively better
system. This must involve substantial investigation of a large and not always coherent
body of research literature on human-computer interaction, as a first step.
8.5.3 Inclusion of a Critic Module
In the area of application, users have remarked that the performance of the system is
generally good. When it is unsatisfactory in particular legal examples, we have identified
classes of reasons (e.g. related to limited or anomalous details in the court reports that
supply us with the information to represent in the case base, imperfect coverage of the
application because of the size of the case base, and judge’s discounting of previous cases
cited by the contending lawyers in the report of some particular case) for the deficiencies.
For each class, it is possible to outline the knowledge needed to identify likely membership
of a situation in that class and to apply a suitable corrective action. Therefore, as an
improvement on the basic architecture that ASHSD exemplifies, it is reasonable to propose
an extra ‘critic’ module, which can modify or advise caution in the use of outputs such as
those quoted in section 6.4.1 where the performance of the present version of ASHSD is
least satisfactory. The idea of a critic module has come up in occasional past researches
on machine learning, but ‘legal criticism’ requires an advance in sophistication. ASHSD’s
problem area is nevertheless not too complicated to be manageable - and a rather useful
target for research - in this respect. Chapter 8. Conclusions 194
8.5.4 Extension of the Scope of ASHSD
Legal knowledge in the system covers three aspects of matrimonial home settlement in divorce in English law, namely owned home settlement, transfer of tenancy, and injunction to protect a spouse, or a family member in the custody of a spouse. Other areas of English divorce settlement (e.g. financial settlement, property settlement, assignment of custody of dependent children, etc.) would have to be included to make the system a full-fledged divorce settlement décision-support tool.
8.6 Concluding Remark
Two areas of research were investigated in this project. The first was the design and implementation of a general hybrid (RBR with CBR) reasoning environment with a form that could be re-used in a wide variety of application areas. The second was the design and implementation of a particular software system for a realistic application. The outcome has been positive in both areas, and can serve as a basis for much future work to extend the functionality and the knowledge base of the system as we have described in various
places in the thesis (for example, in the present chapter and section 7.4). Appendix A
Previously Decided Cases
1. AKINGBEHIN v AKINGBEHIN [1964] 108 S.J. 520
Court of Appeal
Willmer, Davies and Russell, L, JJ.
18th June, 1964
Husband and Wife - In junction - Pending proceedings - Matrimonial home is
jointly owned by the parties - Wife leaving matrimonial home with young children -
Wife and children living in inadequate accommodation - Wife applying for order to
exclude husband from matrimonial home so that she and children could return -
Whether husband should be ordered to vacate matrimonial home.
2. APPLETON V APPLETON [1965] 1 All ER 44
Court of Appeal
Lord Denning, M. R., Pearson and Davies, L.JJ.
12th, 13th November 1964
Husband and Wife - Property - Matrimonial home - Discretion of court - W ife sole
owner - Improvements by husband - Wife leaving husband and petitioning for
195 Appendix A. Previously Decided Cases 196
divorce on ground of cruelty - Husband seeking restitution of conjugal rights -
Husband working at matrimonial home and willing to pay rent - Whether order for
sale forthwith of house should be made - Whether husband entitled to part of
proceeds when house sold - Married Women’s Property Act, 1882 (45 & 46 Viet. c.
3. BASSETT v BASSETT [1975] 1 All ER 513
Court of Appeal
Megaw, Ormrod LJJ and Cumming-Bruce J
6th, 7th November 1974
Inju n ctio n - Husband and wife - Matrimonial home - Exclusion of spouse from
matrimonial home - Divorce proceeding pending - Balance of hardship -
Circumstances making it impossible or intolerable for wife to live in same house as
husband - Wife looking after child of family - Need to provide home for wife and
child - No suitable alternative accommodation for wife and child - M arriage
relationship having broken down completely - Hostility between parties -
Matrimonial home too small to allow parties to live there separately - No evidence
that husband would have difficulty in finding alternative accommodation - Whether
wife entitled to injunction excluding husband from home.
4. BEDSON V BEDSON [1965] 3 All ER 306
Court of Appeal
Lord Denning, M.R., Davies and Russell, L, JJ.
26th May, 22th July 1965
Husband and Wife - Property - Matrimonial home - Discretion of court -
Conveyance to spouses jointly - Trust for sale and proceeds of sale etc. for grantees
as joint tenants - Property purchased out of husband’s savings - Husband carrying
on business on property - Wife deserting husband - W ife’s entitlement to share in
property - Whether order for sale of property should be made - Payment by husband Appendix A. Previously Decided Cases 197
of weekly sum in respect of wife’s interest - Injunction restraining wife from dealing
with her interest - Married Women’s Property Act, 1882 (45 & 45 Viet. c. 75), s.
17.
5. BLACKSTOCK v BLACKSTOCK [1991] 2 FLR 308
Court of Appeal
Slade and Butler-Sloss LJJ
29th November 1990
Domestic violence - Single incident of violence - Wife ’$ application for ouster order
- Judge unable to make a finding as to who was to blame for the incident - Judge
refusing to make the order - Whether judge was plainly wrong in the exercise of his
discretion - Application of criteria contained in s. 1(3) of the Matrimonial Home Act
6. BRENT V BRENT [1974] 2 All ER 1211
High Court
D unn J
23rd May 1974
Injunction - Husband and wife - Matrimonial home - Exclusion of spouse from
hom e - Jurisdiction - Decree nisi granted - Applicant having no propriety interest
in hom e - Council dwelling - Tenancy in husband’s name - Wife granted decree in
consequence of husband’s behaviour - Parties living together in home with grown-up
children - Husband and wife not on speaking terms - Application by wife for
injunction to exclude husband from home - Whether court having jurisdiction to
grant injunction.
7. BROWN V BROWN [1982] 3 FLR 161
Court of Appeal
Ormrod, L. JJ., Fox L JJ and Balcombe J
9th December 1981 Appendix A. Previously Decided Cases 198
Property adjustment - Matrimonial home purchased in joint names - Wife living at
home - Husband has secure alternative accommodation - Property of limited value -
Shared proceeds of sale insufficient to enable either party to purchase alternative
accommodation - Order for sale postponed until death or remarriage of wife -
Payment of occupational rent.
8. BROWNE V PRITCHARD [1975] 3 All ER 721
Court of Appeal
Lord Denning MR, Roskill and Ormrod LJJ
25th June 1975
Divorce - Financial provision - Matters to be considered by court when making
order - Duty to place parties in financial position in which they would have been if
marriage had not broken down - Allocation of family assets - Matrimonial home
only asset - Matrimonial home owned by parties jointly - Wife having made no
contribution to purchase - Wife having left husband - Husband remaining in
matrimonial home with children from previous association - Husband unable to
obtain other accommodation - Wife living in council house - Wife claiming
payment of value of half share in house - Whether wife entitled to half share -
Whether immediate sale of matrimonial home should be ordered - Matrimonial
Causes Act 1973, s 25.
9. BURKE V BURKE [1974] 2 All ER 944
Court of Appeal
Davies, Buckley and Lawton LJJ
9th November 1973
Husband and wife - Property - Matrimonial home - Sale under trust for sale -
Power to postpone sale - Power of court to order sale - D iscretion - Declaration as
to property rights - Declaration that husband holding property in trust for himself
and wife in equal shares - Husband and wife divorced - Wife living in house with Appendix A. Previously Decided Cases 199
two children of marriage - Factors to be taken into consideration by court when
determining whether to order sale - Duty of husband to provide for children -
Whether relevant consideration - Married Women’s Property Act 1882, s 17.
10. BUTTON V BUTTON [1968] 1 All ER 1064
Court of Appeal
Lord Denning, M. R., Danckwerts and Widgery, L.JJ.
29th January 1968
Husband and Wife - Property - Matrimonial home - Husband sole owner at law -
Work done by both spouses on the property - Work done by wife of a kind that a
wife would ordinarily do - Whether wife entitled to beneficial interest in house or
its proceeds of sale - Married Women’s Property Act, 1882 (48 & 48 Viet. c. 75),
s. 17.
11. CARPENTER v CARPENTER [1988] 1 FLR 121
Court of Appeal
Glidewell and Bingham L JJ
18th May 1987
Domestic violence - Non-molestation injunction - Power of arrest - Court making
non-molestation order with power of arrest - Power of arrest extended subsequently
- Wife alleging assault - W ife’s allegation not found proved - Undertakings given by
husband - Power of arrest attached to undertaking - Whether jurisdiction to attach
power of arrest to untertakings.
12. CHADWICK V CHADWICK [1985] FLR 606
Court of Appeal
Cumming-Bruce and Slade L JJ
8th November 1984 Appendix A. Previously Decided Cases 200
Financial provision - Property adjustment order - Matrimonial home sole capital of
the husband and wife - Judge making order for trust for sale with contingencies -
Wife having sole occuption of the matrimonial home - Property not to be sold
unless she remarried, cohabited or died - Whether contingency of remarriage or
cohabitation unfair having regard to wife’s physical disabilities - Imposition of sale
would render her homeless if she should remarry, or cohabit with, an impecunious
man who was unable to afford to provide suitable alternative accommodation -
Balancing reasonable requirements of husband and wife.
13. GLUTTON v GLUTTON [1991] 1 FLR 242
Gourt of Appeal
Lloyd LJ and Ewbank J
26th October 1990
Financial provision - Clean break - Application by wife for ancillary relief following
divorce - Judge order that husband transfer to wife his entire interest in the
matrimonial home - Whether judge properly applied the clean break principle -
Whether Martin order offended against the clean break principle.
14. GOBB V GOBB [1955] 2 All ER 696
Gourt of Appeal
Denning, Birkett and Romer LJJ
7th and 8th June 1955
Husband and Wife - Title to property - Matrimonial home - Both parties
contributing to purchase - Husband alone making mortgage repayments - Court
order for sale with vacant possession - Sale inappropriate when matrimonial
proceedings pending - Married Women’s Property Act, 1882 (45 & 45 Viet. c. 75), g. 77.
15. ELSWORTH v ELSWORTH [1980] 1 FLR 245 Appendix A. Previously Decided Cases 201
Court of Appeal
Stamp and Orr, L JJ, and Sir David Cairns
8th June 1978
Injunction - Exclusion order - Wife seeking order to exclude husband from m
matrimonial home - Wife determined not to return but not found to have
reasonable grounds for not returning.
16. GOODFIELD v GOODFIELD June 19, [1975]; Transcript No. 269 , CA.
Court of Appeal
Lord Justice Cairns and Lord Justice Scarman
19th June 1975
Husband and Wife - Matrimonial home - Divorce - Trust for sale - Wife living in
matrimonial home - Husband living with tenant of council accommodation - Wife ^s
share in equity insufficient to purchase alternative accommodation - Whether order
for sale to be made - Court’s exercise of discretion - Matrimonial Causes Act 1973 ("c. fg;,
17. GURASZ V GURASZ [1969] 3 WLR 482
Court of Appeal
Lord Denning M.R., Edmund Davies and Fenton-Atkinson L.JJ.
16th June, 9th July 1969
Husband and Wife - Matrimonial home - Act of 1967, s. 1 - Jurisdiction - Joint own
ership of matrimonial home - Order for husband to leave - Whether jurisdiction to
make - ’Right of occupation’ - Matrimonial Homes Act, 1967 (c.75),ss.l(l)(2)(3)(8),
2(1),5(1),6(1).
Husband and Wife - Matrimonial home - W ife’s right to occupy - Nature of court’s
protection - Joint ownership of home - Wife forced to leave - Whether remedy avail
able. Appendix A. Previously Decided Cases 202
18. HALE V HALE [1975] 2 All ER 1090
Court of Appeal
Megaw and Stephenson LJJ
21st February 1975
Divorce - Property - Adjustment order - Meaning of property - Tenancy - Weekly
contractual tenancy - No covenant against assignment - Whether tenancy
’property’ - Whether court having jurisdiction to order transfer of tenancy -
Matrimonial Causes Act 1973, s.24(l)(a)~
19. HALL V HALL [1971] 1 W.L.R. 404
Court of Appeal
Lord Denning M.R., Sachs and Buckley L.JJ.
18th, 19th January 1971
Husband and Wife - Matrimonial home - Pending proceedings - Spouse’s right of
occupation - Joint occupation - W ife’s petition for judicial separation and order to
compel husband to leave home - Accommodation sufficient to enable parties to live
under same roof and preserve family unit - Order turning one spouse out of home
not to be made unless necessary for protection of wife or children.
20. HANLON v HANLON [1978] 1 WLR 592
Court of appeal
Stamp, Orr and Ormrod L.JJ
14th, 17th October 1977
Husband and Wife- Matrimonial home- Divorce-Trust for sale- Deferred order for
sale of matrimonial home- Wife living with children in matrimonial home-Husband
living rent free in flat provided by employer-Wife’s share in equity in sufficient to
purchase alternative accommodation-Court’s exercise of discretion-Matrimonial
Causes Act 1973 (c. 18), s. 25 (1) Appendix A. Previously Decided Cases 203
21. HARVEY V HARVEY [1982] 1 All ER 693
Court of Appeal
Ormrod, Oliver LJJ and Purcheis
12th October 1981
Divorce - Property - Adjustment order - Matrimonial home - Matters to he
considered - Home held in joint names on trust for sale - Home of small value -
Husband securely housed in council accommodation - Wife having to provide home
for three youngest children aged 18 and under - Whether sale of matrimonial home
should he postponed during wife’s lifetime or only until home no longer required for
children - Whether wife should pay husband occupation rent - Matrimonial Causes
22. HECTOR v HECTOR [1973] 3 All ER 1070
Court of appeal
Lord Denning MR, Megaw and Scarman LJJ
22nd March 1973
Divorce - Financial provision - Matters to he considered by court when making
order - Duty to place parties in financial position in which they would have been in
absence of breakdown - Transfer of property - Variation of settlement - Matrimonial
home - Transfer of husband’s interest in home to wife - Charge in favour of
husband - Deferred charge - Husband leaving wife - Wife remaining in home and
bringing up children - Wife meeting mortgage repayments and other outgoing after
husband’s departure - Husband and wife each beneficially entitled to half shares in
hom e - Equity valued at £4,000 - Husband’s share transferred to wife - Husband
given charge of £1,000 - Payment of charge deferred until happening of certain
events - Matrimonial Proceedings and Property Act 1970, ss 4 ( 0‘)(c)(d), 5 (1).
23. HOPPER V HOPPER [1978] 1 WLR 1342 Appendix A. Previously Decided Cases 204
Court of Appeal
Stamp, Lawton and Ormrod L. JJ.
22th May 1978
Injunction - Domestic violence - Exclusion from matrimonial home - Duration of
injunction - Wife leaving home with child because of husband’s violence behaviour
- Marriage of short duration - No immediate possibility of divorce proceedings -
Time span of injunction excluding husband from home - Domestic Violence and
Matrimonial Proceedings Act 1976 (c. 50), s. 1.
24. HORNER v HORNER [1982] 2 Ail ER 495
Court of Appeal, Civil Division
Ormrod, Dunn LJJ and Sir Sebag Shaw
18th February 1982
In ju n ctio n - Husband and Wife - Molestion - Meaning - Harassment by husband -
Jurisdiction of county court - Wife obtaining order in magistrates ’ court restraining
husband from using violence - Husband refraining from violence but continuing to
harass wife - Whether magistrates’ court order extending to husband’s harassment
- Whether wife entitled to obtain county court order restraining husband from mo
lesting her - Domestic Violence and Matrimonial Proceedings Act 1976, s 1(1) (a) -
Domestic Proceedings and Magistrates’ courts Act 1978, s 16.
Injunction - Molestion - Domestic violence - County court - Jurisdiction - Wife ob
taining order in magistrates ’ court restraining husband from using violence - Juris
diction of county court to restrain husband from molesting wife - Domestic Violence
and Matrimonial Proceedings Act 1976, s 1(1) (a) - Domestic Proceedings Magis
trates’s Courts Act 1978, s 16.
Injunction - Husband and Wife - Domestic violence - Attachment of power of arrest
to injunction - Husband not violent for nine months but continuing to harass wife
- Wife entitled to injunction in county court restraining husband from assulting,
molesting or interfering with her - Whether power of arrest should be attached to
injunction - Domestic Violence and Matrimonial Proceedings Act 1976, s 2(1). Appendix A. Previously Decided Cases 205
25. JANSEN V JANSEN [1965] 3 All ER 363
Court of Appeal
Lord Denning, M.R., Davies and Russell, L.JJ.
28th, 31th May, 15th July 1965
Husband and Wife - Property - Matrimonial home - Discretion of court - Wife sole
ow ner - Joint enterprise - Improvements by husband - Conversion of wife’s house
into fla ts - Work done by husband - Proportions in which profits to be shared not
agreed - Divorce proceedings - Determination of husband’s share of profits - Married
Women’s Property Act, 1882 (45 & 46 Viet. c. 75), s. 17 - Matrimonial Causes
(Property and Maintenance) Act, 1958 (6 & 7 Eilz. 2 c. 35), s. 7(3).
26. JONES V JONES [1971] 1 WLR 396
Court of Appeal
Davies, Edmund Davies and Karminski L.JJ.
14th, 15th January 1971
Husband and Wife - Matrimonial home - Pending Proceedings - Wife forced to leave
- Husband in occupation with mistress - Wife’s petition for divorce pending - Appli
cation for injunction ordering husband to leave - Jurisdiction of court to make order
during pendency of proceedings.
27. LEWIS V LEWIS and Another [1985] 2 All ER 449
House of Lords
Lord Fraser of Tullybelton, Lord Elwyn-Jones, Lord Diplock,
Lord Edmund-Davies and Lord Bridge of Harwich
27th March, 9th May 1985
Divorce - Property - Protected or statutory tenancy - Transfer of protected or statu
tory tenancy on termination of marriage - Legislation giving court power to make
transfer order ‘on granting decree ... or at any time thereafter’ - Decree made ab
solute before legislation coming into force - Whether power to make transfer order
exercisable retrospectively - Matrimonial Homes Act 1967, Sch 2. Appendix A. Previously Decided Cases 206
28. LUCAS V LUCAS [1992] 2 FLR 52
Court of Appeal
Balcombe, Woolf and Staughton LJJ
24th April 1991
Injunction - W ife’s application in divorce proceedings for injunction to exclude hus
band from the property after decree absolute - Council property tenancy in sole name
of wife - Whether judge had jurisdiction to order husband to vacate property after
decree absolute.
29. MARTIN (B.H.) v MARTIN (D.) [1978] Fam. 12
Court of Appeal
Stamp and Ormrod L. JJ. and Sir John Pennycuick
10th March 1977
Husband and Wife - Matrimonial home - Divorce - Trust for sale - Wife living in
matrimonial home - Husband living with tenant of council accommodation - W ife ’s
share in equity insufficient to purchase alternative accommodation - Whether order
for sale to be made - Court’s exercise of discretion - Matrimonial Causes Act 1973 ("c. 7,9;,
30. MESHER v MESHER and HALL [1980] 1 All ER 126
Court of Appeal
Davies, Cairns and Stamp L JJ
12th February 1973
Divorce - Property - Adjustment order - Transfer of property - Matrimonial home -
Matters to be considered by court when making order - Financial needs, obligations
and responsibilities of parties.
31. MINTON V MINTON [1979] 1 All ER 79 Appendix A. Previously Decided Cases 207
House of Lords
Lord Wilberforce, Viccount Dilhorne, Lord Fraser of Tullybelton,
Lord Russell of Killowen and Lord Scarman
16th, 17th October, 23rd November 1978
Divorce - Financial provision - Jurisdiction to vary consent order incorporating terms
agreed between spouses - Whether a consent order which incorportates terms agreed
between spouses a final order - Matrimonial Causes Act 1973, s 23(1).
32. MOISI V MOISI [1974] 5 Fam 26
Court of Appeal
Edmund Davies, Lawton and Ormrod, L. JJ.
10th May 1974
Husband and Wife - Matrimonial home - Divorce - Trust for sale - Wife living in
matrimonial home - Husband living with tenant of council accommodation - W hether
order for sale to be made - Matrimonial Causes Act 1973 (c. 18), ss.2f(l), 25(1).
33. MONTGOMERY v MONTGOMERY [1964] 2 All ER 22
Probate, Divorce and Admiralty Division
O rm rod, J.
11th, 18th March 1964
Injunction - Husband and wife - Judicial separation - Parties continuing to live in
same premises after decree - Tenancy of premises in husband’s name - Jurisdiction
to restrain husband by injunction from molesting wife - No jurisdiction to compel
husband to leave premises.
34. MORTIMER v MORTIMER-GRIFFIN [1986] 2 FLR 315
Court of Appeal
Sir John Donaldson MR, Parker and WOOlf L JJ
29th April 1986 Appendix A. Previously Decided Cases 208
Property - A d ju stm en t - After divorce wife becoming solely responsible for maintain
ing herself and child including child’s education - husband becoming unemployed and
in receipt of social security - Whether Mesher type of order appropriate.
35. MAYERS v MAYERS [1982] 1 All ER 776
Court of Appeal
Arnold, P., O’Connor LJ, and Stephen Brown J.
4th December 1981
Injunction - Exclusion of party from matrimonial home - W ife’s application to
exclude husband - Matters to be consider - Wife leaving matrimonial home with
young children - Wife and children living in overcrowded conditions - Wife applying
for order to exclude husband from matrimonial home so that she and children could
return - Wife genuinely feeling that she could never live with husband again -
Whether sufficient grounds for court to exclude husband from matrimonial home.
36. PETTITT v PETTITT [1969] 2 All ER 385
House of Lords
Lord Reid, Lord Morris of Borth-Y-Gest, Lord Hodson
Lord Upjohn and Lord Diplock
6th, 10th, 18th, 19th February, 23rd April 1969
Husband and Wife - Property - Matrimonial home - Wife sole legal owner - Im
provements to matrimonial home effected by husband - No bargain between husband
and wife that he should acquire beneficial interest in matrimonial home in return for
his work - Wife left husband and obtained divorce - Family assets - Common inten
tion of parties - Application of presumption of resulting trust and presumption of
advancement - Whether husband entitled to beneficial interest in matrimonial home
in respect of the improvements - Whether husband entitled to payment of the amount
thereof - Married Women’s Property Act 1882 (45 & jd Viet. c. 75), s. 17
37. PHILLIPS V PHILLIPS [1973] 2 All ER 423 Appendix A. Previously Decided Cases 209
Court of Appeal
Edmund Davies, Stephenson and Roskill L.JJ
23rd Feburary 1973
In ju n ctio n - Husband and Wife - Matrimonial home - Exclusion of spouse from
matrimonial home - Parties divorced - Parties joint tenants of former matrimonial
home - Parties continuing to live in home - Circumstances in which injunction will
be granted - Injunction against husband - Conditions making it intolerable for wife
and child to continue sharing accommodation with husband - Husband’s behaviour
endangering mental health of wife and child - No evidence of physical assault or
apprehension of violence.
38. PINCKNEY v PINCKNEY [1966] 1 All ER 121
Probate, Divorce and Admirally Division
O rm rod, J,
14th December 1965
Injunction - Husband and Wife - Matrimonial home - Divorce petition by wife pend
ing - Three-roomed home - Husband occupying small separate bedroom - Husband
moved into sitting-room, and admitted mistress and their baby to live there with him
- Injunction on husband to remove mistress and restraining him from continuing to
occupy sitting-room - Injunction that husband should leave matrimonial home not
granted.
39. REGAN v REGAN [1977] 1 All ER 428
Family Division
SIR George Baker P
n th M ay 1976
Divorce - Property - Adjustment order - Transfer of property - Local authority
tenancy - Condition of tenancy prohibiting assignment without permission of
authority - Application by wife for order transferring tenancy to her - Authority Appendix A. Previously Decided Cases 210
indicating they would be unlikely to implement transfer order - No children to be
consider - Merits as between husband and wife equal - Whether court should make
transfer order - Matrimonial Causes Act 1973, s 24(1)■
40. RENNICK v RENNICK [1978] 1 All ER 817, [1977] 1 WLR 1455, CA.
Court of Appeal
Stamp and Ormrod L. JJ.
29th July 1977
Injunction - Exclusion of party from matrimonial home - County court - Children’s
interests paramount - Home needed to accommodate children - Need for wife to be
with children - Husband violent towards wife - Parties married with five children
aged from four to 11 - Wife leaving with children and going to live at mother’s
house - Wife and children living in cramped conditions at mother’s house - Husband
occupying matrimonial home - Matrimonial home large enough to accommodate
children - Whether order should be made excluding husband from matrimonial
hom e - Domestic Violence and Matrimonial Proceeding Act 1976, s l(l)(c).
41. SAMSON V SAMSON [1982] 1 All ER 780
Court of Appeal
Ormrod, Dunn LJJ and Heilbron J
12th January 1982
Inju n ctio n - Exclusion of party from matrimonial home - W ife’s application to
exclude husband - Matters to be consider - Wife leaving matrimonial home with
young children - Wife and children living in overcrowded conditions - Wife applying
for order to exclude husband from matrimonial home so that she and children could
return - Wife refusing to return while husband in occupation - Whether sufficient
grounds for court to exclude husband from matrimonial home.
42. SCOTT V SCOTT [1992] 1 FLR 529 Appendix A. Previously Decided Cases 211
Court of Appeal
Glidewell LJ and Rattee J
24th April 1991
In ju n ctio n - Ouster - Husband unable to accept marriage was at an end - Husband
seeking repeatedly, without use of violence, to effect reconciliation - Judge finding
husband’s actions were breach of earlier undertaking and court order and conduct
serious enough to justify ouster order - Whether judge exercised discretion wrongly.
43. SHIPMAN V SHIPMAN [1924] 2 Ch 140
Court of Appeal,
Pollock, M.R., Atkin, L.J. and Sargant, L.J.
19th March 1924
Husband and Wife - Wife’s separate Property - Dwelling House - Protection and
Security - Right to exclude Husband from matrimonial Home - Husband’s Misconduct
- Cruelty - Interim Injunction - Married Women’s Property Act, 1882 (45 & jd Viet,
c. 75), s. 12.
44. SILVERSTONE v SILVERSTONE [1953] 1 All ER 556
Probate, Divorce and Admiraly Division
Pearce, J.
30th January 1953
Inju n ctio n - Husband and Wife - Husband restrained from occupying matrimonial
home home pending petition by wife for judicial separation.
45. TARR V TARR [1971] 2WLR 376
Court of Appeal
Lord Denning M. R., Edmund Davies and Megaw L. JJ.
14 D ecem ber 1970 Appendix A. Previously Decided Cases 212
Husband and Wife - Matrimonial home - Act of 1967, s.l - Jurisdiction - Tenancy
in husband’s name - W ife’s separation order - Whether jurisdiction to make order
excluding husband - ‘Regulating the exercise ’ of ‘rights of occupation’ - Matrimonial
Homes Act 1967 (c. 75), s.1(2).
46. THOMPSON v THOMPSON [1976] Fam. 25
Court of Appeal
Buckley and Ormrod LJJ and Sir John Pennycuick
31th January 1975
Husband and Wife - Property - Transfer of property - Council house - H usband
tenant - Wife’s application for transfer of tenancy - Whether tenancy of council
house ’property’ - Matrimonial Causes Act 1973 (c. 18), s.24(l)(a).
47. VAUGHAN v VAUGHAN [1973] 3 All ER 449
Court of Appeal
Davies, Stephenson L JJ and SIR Seymour Karminski
11th June 1973
Injunction - Husband and Wife - Restraint against molestation - M olestion - M ean
ing - Pestering - Absence of violence - Relevance of past conduct - Injunction re
straining husband from molesting wife - Husband having inflicted violence on wife
on earlier occasions - Husband previously committed several times for breaches of
similar injunction - Husband calling on wife at home and place of work - Husband
pestering wife to go out with him - Husband not committing violence against wife -
Wife frightened of husband and pestering affecting health - Whether husband guilty
of molestation.
Contempt of court - Committal - Breach of injunction - Restriction on liberty to
apply to court for order for release prior to specified date - Propriety.
48. WALKER v WALKER [1978] 3 All E.R. 141 Appendix A. Previously Decided Cases 213
Court of Appeal
Stamp, Ormrod and Geoffrey Lane LJJ
13th December 1977
In ju n ctio n - Exclusion of party from matrimonial home - Decree nisi granted - W ife’s
application to exclude husband - Factors to be considered - Wife granted decree nisi
on ground of husband’s behaviour - Wife having custody of children and looking
after them - Serious friction between parties and between them and children - Non
molestation order against husband - Whether fair, just and reasonable in all the
circumstances to exclude husband from matrimonial home.
49. WACHTEL v WACHTEL [1973] Fam 72
Court of Appeal
Ormrod, J., Lord Denning M.R., Phillimore
and Roskill, L.JJ.
21st, 25th, 26th July 1972, 3rd October 1972, 8th Feburary 1973
Husband and Wife - Maintenance - Divorce - Conduct of parties - Both parties
granted decrees of divorce - Principles for granting ancillary relief - Effect of con
duct on orders concerning property and financial provision for wife - Matrimonial
Proceedings and Property Act 1970 (c. fô), ss. 2 (1), 4 (1), 5 (1).
50. WHITE V WHITE [1983] 4 FLR 696
Court of Appeal
Cumming-Bruce, L. J. and Reeve, J.
7, 8 F ebruary 1983
Injunction - Divorced parties living apart - Former husband abusing and threaten
ing former wife when exercising access to children - Former wife granted injunction
ordering former husband not to molest her and restricting him from entering or
approaching former matrimonial home - Former husband in breach of injunction -
Former wife applies for power of arrest to be attached to injunction - Whether court
had power to attach power of arrest. Appendix A. Previously Decided Cases 214
Inju n ctio n - Jurisdiction - Power to attach power of arrest - Power provided by s.
1(2) of the Domestic Violence and Matrimonial Proceedings Act 1976 - Whether that
Act applied to parties to a marriage which had been dissolved by decree absolute. A ppendix B
Questionnaire Used in the Validation Process
PRACTICAL ASSESSMENT OF A LEGAL REASONING SYSTEM
Please go through the questionnaire in this document and answer the questions as
carefully as possible. Most of these questions relate to the computer system you have just
used. All information will be used to assess the appropriateness of the system.
N am e ::
Institution ::
Department ::
215 Appendix B. Questionnaire Used in the Validation Process 216
Assessment of the legal reasoning system
Given below are a number of statements. Please indicate your response for each state ment. For example, if you feel that you agree strongly about the statement then write A in the box next to the statement. However,if you disagree strongly about the statement then write E in the box. There are no right or wrong answers. Please give the response closest to your own choice:
A = Strongly agree
B = Agree
C = Uncertain
D = Disagree
E = Strongly disagree
[1] M odel of the system:
• The system demonstrates the use of relevant legal knowledge sources (e.g.,
legislative sources in rule form and previously-decided case reports).
• Law students will be able to gain a good introduction to the concepts of legal
reasoning.
• This system requires little knowledge of computing.
• The present hybrid reasoning model is applicable to many areas of law (e.g.,
company law, trademarks law, etc).
[2] U sability: It is observed that this hybrid reasoning model is a useful tool for several
reasons: Appendix B. Questionnaire Used in the Validation Process 217
• To provide decision support.
• To act as a check list for decision-makers.
[3] Quality of advice:
• Rule-based advice is adequate.
• It retrieved previously-decided similar cases relevant to the new case.
• Partial rule-based advice is adequate.
• Partial rule-based advice reflects human reasoning processes.
• Quality of justification is good.
[4] Final Conclusion:
Categories Assigned grade
M odel good average poor
Usability good average poor
Quality of advice good average poor A ppendix C
Questionnaire Used in the Critical Inspection of ASHSD
PRACTICAL ASSESSMENT OF A LEGAL REASONING SYSTEM
Please go through the questionnaire in this document and answer the questions as carefully as possible. Most of these questions relate to the computer system you have just used. All information will be used to assess the appropriateness of the system.
N am e ::
Institution ::
Department ::
218 Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 219
C.l Background Information about the System
This assessment is for an automated legal reasoning system ASHSD which (stands for
Advisory Support for Home Settlement in Divorce). ASHSD has been developed purely as a research exercise. The system considers three aspects of matrimonial home settlement, namely owned home settlement, transfer of tenancy, and the severity of injunction. It uses two types of reasoning methods: rule-based reasoning (RBR) and case-based reasoning
(CBR). The main structure of ASHSD consists of three parts: RBR module, CBR module, and a module for estimation of the relative suitability of the two reasoning methods. In the process of consultation with ASHSD, the user can examine either or both rule-based and case-based information to formulate a suitable solution for the new case in hand. The computational framework of ASHSD is shown in Figure C.l.
The rule-base component consists of two types of rule: available-action(s) rules^ and prediction rules. The available-action(s) rules are explicit in the legal sources (statutes, case reports, etc.), and determine whether or not a court has the power to act or take a specific action. For example, in a severity of injunction case the court has a range of options available as below:
• non-molestation order
• non-molestation order attach with a power of arrest
• exclusion order
• exclusion order in conjunction with a power of arrest
The available-action (s) rules determine which of these options are applicable for a case.
The prediction rules explain how the courts are likely to act within the range of options available, which is circumscribed by the available-action(s) rules. Thus, the prediction rule illustrates how the court is likely (on the basis of relevant past episodes) to exercise its discretionary power for particular cases. We can say, for the purpose of distinction, that the available-action(s) rules give available-action(s) on the available options and the prediction Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 2 2 0
COMPREHENSIVE ADVICE AND JUSTIFICATION
PARTIAL ADVICE AND JUSTIFICATION
RUI.E-IIASEI) REASONING RULE-BASED ADVICE
c> CASE-BASED REASONING SELECTION OF SIMILAR CASES AND DISPLAY OF THE CASE DETAII-S
WITH FINAL JUDGEMENT surrAim.iTY o e r e a s o n i n g METHOD(S)
CASE-BASED ADVICE
M EN U o m O N S
INDICATION OF SUITABILITY OF REASONING METHOD(S)
SUITABILITY OF REASONING METHOD(S)
Figure C.l: The computational framework
rules privide prediction about what a court may conclude for a particular situation. Both types are contained within the rule-based advice part of ASHSD. Although it is not unusual in the literatire on expert system for legal application to find papers that do not distinguish between these two types of rule, the rest of the questionnaire distinguishes between them.
Unless it is specifically mentioned, it will be assumed from here on that the rule-based analysis is founded on the prediction rules.
The rule base consists of 190 rules. In this project some particular part of matrimonial- home-related legal decision-making are transformed into an IF
The justification is that the available-action(s) rules either apply to a case on the basis of certain special facts about it or do not, and that questions of how a court might proceed if that information were interpreted somewhat differently do not arise. Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 2 2 1
B R U L E 1 1 2
IF
Peripheral: (home type is rented) (applicant is the wife) (respondent is the husband) (length of the m arriage is long)
Significant: (divorce proceedings are pending) (respondent denies the alleged assault) (applicant is the oHicial tenant)
E s s e n t i a l : (respondent has used violence against a child of the family) (evidence of the allegation is corroborated) (severity of the allegation is dangerous) (there are dependent children) (dependent children are living with the applicant) THEN
In this case it is possible for a general exclusion order to be granted.
Figure C.2: Different types of preconditions of BRULE112
The prediction rules can generate any of the three types of output: clear-prediction^ speculation, and no-prediction.
Clear Prediction
Clear-prediction is possible when at least one of the prediction rules has triggered as a consequence of the facts of the new case. The system presents what we can call speculations when none of the rules is triggered by the facts of the case in hand.
Speculation
The system presents what we can call speculations by applying a weighting criterion to the rule preconditions that are true, even when none of the rules is triggered. A speculation Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 222
RESRES RES NRE
CPR NPR NPRSPE
Comprehensive Partial Advice No Advice Advice
: LEGEN D:
RES = Response NRE = No-response CPR = Clear-prediction
SPE = Speculation NPR = No-prediction
Figure C.3: Different types of behaviour leading to rule-based output
consists of conclusions that would have followed if all the preconditions of a rule that has some relevance has been true, plus output focusing on the failed preconditions (i.e. reasons
why a conclusion cannot be accepted without reservations).
The first step in generating a speculation is to identify the rules that are nearly trig
gered. A scoring mechanism is used to determine which rules are closeest to triggering.
A scoring mechanism is used to find out which rules are closest to triggering. The score
calculation formula for a particular rule R i is:
w here Scorcu = WiNe~\- W2 Ns + w^Np and Scorei is the total number of preconditions
of the rule in the equation. Æg, N s, Np are the number of essential, significant and
peripheral preconditions that are true for the current case. The weighting factors Wi, W2 ,
are for the essential, significant and peripheral categories of preconditions. An example Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 223 of a prediction rule is shown in Figure C.2.
No Prediction
We have found by experiment that there is a consistent threshold in our score, below which any information that ASHSD may give is unhelpful. The system, therefore, offers no information unless at least one of its prediction rules has a score above the threshold.
If there is no such score, we say that the output is of a no-prediction type.
C.2 Different Rule-Based Advice
When a user ask for rule-based advice, ASHSD can provide one of three possible options:
comprehensive advice, partial advice, and no advice. In comprehensive advice, the system provides the possible available-action(s) plus a prediction of a court’s decision, provided
that at least one of the prediction rules has triggered. The partial rule-based advice can be in one of two categories. For category-one of partial rule-based advice, ASHSD offers the relevant available-action (s) and also presents a speculation (in the sense in which we have defined that term). Thecategory-two of partial rule-based advice produces
no prediction or speculation but does suggest some valid available-action (s). Finally, the system provides no rule-based advice at all when it fails to come up with available-action(s)
or any kind of predictive information. These different types of rule-based advice are shown
diagrammatically in Figure C.3.
C.2.1 Case-Based Advice
The case base of ASHSD consists of two parts: a case library, which serves as a repository
for cases, and a set of access procedures. The case library of ASHSD at present is com
prised of 50 manually-coded previously-decided cases. The access procedures are based on
a three-step indexing facility. When the case-based side of ASHSD is invoked, the cases
that have the highest similarity rating with respect to a current problem are retrieved, and Appendix C. Questionnaire Used in the Critical inspection of ASHSD 224 presented according to how closely they match the problem. To determine the measure of similarity, ASHSD uses the ideas and general approach of numerical taxonomy^. If a measure of similarity taken over the cases in the case base is always below a threshold, which we have arrived at and checked by extensive trials, ASHSD makes no recommen dations. The relevant similarity is judged by a comparison of main surface features of the cases. The system uses features of previously-decided cases to select similar cases from a case base, and displays the previous decision to the user.
When no preference is indicated, the system applies each method separately, and then present results based on an automated relative rating of the qualities of the RBR and
CBR advice.
C.2.2 Questionnaire for the test cases
Use any three test cases for the present evaluation process. Given below are a number of statements. Please indicate your response for each statement according to your own choice:
Test Case Number: 1
Case Name: Source of the case report:
[a] Please tick the type(s) of response the system is providing for the present case:
1. Comprehensive rule-based advice.
2. Partial rule-based advice.
3. Rule-based advice is not appropriate.
^ Numerical taxonomy is the study of classification based on drawing inferences about similarity by the use of metrics established on the properties of the items that are to be classified. Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 225
4. Case-based advice is available.
5. Case-based advice is not appropriate.
Please go to th e next question if you are choosing any of th e options [1], [2], or [4].
Otherwise go to the next case.
[b] Please indicate the advice quality:
Categories Assigned grade
Rule-based advice good average poor
Case-based advice good average poor
[c] Please indicate the suitability of the proposed reasoning method (s):
• Neither RBR or CBR is suitable.
• RBR only is suitable.
• CBR only is suitable.
• Both RBR and CBR are suitable.
Test Case Number: 2
Case Name: Source of the case report:
[a] Please tick the type(s) of response the system is providing for the present case:
1. Comprehensive rule-based advice.
2. P artial rule-based advice.
3. Rule-based advice is not appropriate. Appendix C. Questionnaire Used in the Critical inspection of ASHSD 226
4. Case-based advice is available.
5. Case-based advice is not appropriate.
Please go to the next question if you are choosing any of the options [1], [2], or [4].
Otherwise go to the next case.
[b] Please indicate the advice quality:
Categories Assigned grade
Rule-based advice good average poor
Case-based advice good average poor
[c] Please indicate the suitability of the proposed reasoning method(s):
• Neither RBR or CBR is suitable.
• RBR only is suitable.
• CBR only is suitable.
• Both RBR and CBR are suitable.
Test Case Number: 3
Case Name: Source of the case report:
[a] Please tick the type(s) of response the system is providing for the present case:
1. Comprehensive rule-based advice.
2. Partial rule-based advice.
3. Rule-based advice is not appropriate. Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 227
4. Case-based advice is available.
5. Case-based advice is not appropriate.
Please go to th e next question if you are choosing any of th e options [1], [2], or [4].
Otherwise go to the next case.
[b] Please indicate the advice quality:
Categories Assigned grade
Rule-based advice good average poor
Case-based advice good average poor
[c] Please indicate the suitability of the proposed reasoning method(s):
• Neither RBR or CBR is suitable.
• RBR only is suitable.
• CBR only is suitable.
• Both RBR and CBR are suitable.
C.3 Extra Questions
[i] Please indicate the suitability of this possible future extension:
Each rule has been constructed from one or more statutes. Currently, when a piece of advice is given, related statutes are not shown alongside of the preconditions. If the above facility were addes, then the user would be able to compare the preconditions with appropriate statutes and cross-check the current knowledge in ASHSD. Appendix C. Questionnaire Used in the Critical Inspection of ASHSD 228
Do you think that the proposed extension will help the system’s functionality ?
[1] Yes [2] No
[ii] Are there other extensions that would be desirable from your point of view ?
[iii] Additional Comments:
THANKS FOR YOUR HELP Bibliography
Adrien, W., Branstad, M. & Cherniavsky, J. (1982), “Validation, Verification and Testing
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