DOCSLIB.ORG

Situation Calculus Specifications for Event Calculus Logic Programs From: AAAI Technical Report SS-95-07

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Situation Calculus Specifications for Event Calculus Logic Programs From: AAAI Technical Report SS-95-07 Situation Calculus Specifications for Event Calculus Logic Programs From: AAAI Technical Report SS-95-07. Compilation copyright © 1995, AAAI (www.aaai.org). All rights reserved. Rob Miller Departmentof Computing, Imperial College of Science, Technology& Medicine, 180, Queen’s Gate, London SW72BZ, ENGLAND email: [email protected] Abstract A version of the Situation Calculus is presented whichis Many-sortedfirst order predicate calculus together with able to deal with informationabout the actual occurrenceof parallel and prioritised circumscription is used to describe actions in time. Baker’s solution to the frame problem the Narrative Situation Calculus. Variable names begin using circumscriptionis adaptedto enable default reasoning with a lower case letter. All variables in formulas are about action occurrences, as well as about the effects of universally quantified with maximumscope unless actions. A translation of Situation Calculus style theories into Event Calculus style logic programsis defined, and otherwise indicated. To simplify descriptions of the results are given on its soundnessand completeness. implementations, logic programsare written in a subset of the same language, supplemented with the symbol "not" 1. Introduction (negation-as-failure). Meta-variablesare often italicised, that, for example, "F" might represent an arbitrary ground This paper compares two formalisms and two associated term of a particular sort. The parallel circumscription of default reasoning techniques for reasoning about action m predicates P1 ..... V the Situation Calculus using a variant of Baker’s Pn in a sentence T with V1 ..... k circumscriptive solution to the frame problem [Baker, allowedto vary is written as 1991], and the logic-programming based Event Calculus [Kowalski & Sergot, 1986], in which default reasoning is CIRC[T; Pl ..... Pn; V1..... Vk] realised through negation-as-failure. The version of the Situation Calculus used enables information about the If RI ..... Rmare also circumscribed, at a higher priority occurrences of actions along a time line to be represented. thanPI ..... Pn, this is written as A course of actions identified as actually occurring is referred to as a narrative, and this formalismis referred to CIRC[T; R1 ..... Rm; P1 ..... Pn,VI..... Vk] as the Narrative Situation Calculus. Information about a A CIRC[T; PI ..... Pn ; VI ..... Vk] narrative might be incomplete, so that default assumptions might be required. The circumscription policy incorporated Justification for this notation can be found, for example, in the Narrative Situation Calculus minimises action in [Lifschitz, 1995]. One other piece of notation for occurrences along the time-line. The original Event specifying uniqueness-of-names axioms will be useful. Calculus incorporates an analogous default assumption UNA[F1,..,Fm]represents the set of axioms necessary to that the only action occurrences are those provable from ensure inequality betweendifferent terms built up from the the theory. (possibly 0-ary) function symbols1 . ... Fm. It stands for the axioms The present paper shows that under certain circumstances the Narrative Situation Calculus may be regarded as a Fi(xl........ Xk)# Fj(Yl........ Yn) specification for Event Calculus style logic programs. The programs presented here are described as "Event Calculus for i<j where Fi has arity k and Fj has arity n, together style" because of their use of "Initiates" and "Terminates" with the following axiomfor each F of arity k>0. predicates to describe the effects of actions, becauseof the i form of their persistence axioms, and because of the use of Xk)=Fi(yI ........ A xk=Yk] a time-line rather than the notion of a sequenceor structure Fi(x1 ........ Yk)--"> [x 1 =Y1 A...... of situations. They differ from someother variants of the Event Calculus in that they do not assume complete 2. A Narrative Situation Calculus knowledgeof an initial state, and in that properties can In this section an overview is given of the Narrative hold (and persist) even if they have not been explicitly Situation Calculus employed here as a specification initiated by an action. The programsdescribed are "sound" language. This work is presented more fully in [Miller & for a wide class of domains in that they only allow Shanahan, 1994]. A class of many sorted first order derivation of "Holds" information which is semantically languages is defined, and the types of sentence which can entailed by their circumscriptive specifications. Where appear in particular domaindescriptions are then described. total information is available about the initial state of Finally, the circumscription policy is discussed. affairs, the programs are also "complete" in this same sense. Definition {Narrative domain language }. A Narrative Twomore domain independent axioms are included in the domainlanguage is a first order languagewith equality of Narrative Situation Calculus, concerning properties of four sorts; a sort ct of actions with sort variables narratives and time-points: {a,al,a2,... }, a sort 0g of generalisedfluents I with sort variables {g,gl,g2.... }, a sort o of situations with sort State(t)=S0 6-- --,3al,tl [Happens(al,tl) ^ tl<t] variables{s,sl,s2 .... }, and a sort R of time-points with State(t)--Result(a1,State(t 1)) (N2) sort variables{ t, t 1, t2 ....... }. Thesort Sghas three sub- [Happens(al,tl) ^ tl<t sorts; the sub-sort 0+ of positive fluents with sort --,3a2,t2[Happens(a2,t2)^ [al;~a2 v tlc:t2] variables {f+,f~,f~,...1 2 }, the sub-sort 0- of negativefluents ^tl<t2^t2<t]] withsort variables{ f.,f_l,f.2,... }, andthe sub-sortSf of Axiom(N1) relates all time points before the first action fluents with sort variables{f, fl,f2,... }, suchthat occurrence to the initial situation SO, and Axiom(N2) says that if action A1 happens at TI, T1 is before T and 0+ n 0- = O 0+ L; 0- = 0f Of c 0g no other action happens between TI and T, then the situation at is equal to Result(A1,State(Tl)). It has time-point constant symbolscorresponding to the real numbers,a finite numberof action and positive fluent Several types of axioms are either required or ~illowed in constants, a single situation constant SO, and no negative Narrative Situation Calculus theories 3. The following or generalised fluent constants. It has five functions: definitions specify the form of such sentences. Result: tx x c~ ~ o, And: 0g x Sg --~ 0g, Neg: $+ ---) 0-, Sit: Sg ~ t~, and State: R --~ ~, and six predicates (other Definition {Initial conditions description}. A formula is than equality): Holds ranging over 0g x ~, Ab ranging an initial conditions description if over a x Sf x o, Absit ranging over Sg, < (infix) it is of the form ranging over R x R, < (infix) ranging over R x R, and Happens ranging over ct x R. [] Holds(F, S0) or Holds(Neg(F),S0) Only models are considered in which the predicates < and < whereF is a positive fluent constant. [] are interpreted in the usual way as the "less-than" and "less-than-or-equal-to" relationships betweenreal numbers. Definition {Action description}. A formula is an action "Happens(A,T)"represents that an action A occurs at time description if it is of the form T2, and "State(T)" represents the situation at time Holds(F, Result(A,s)) Several domain independent axioms will always appear in or Holds(Neg(F),Result(A,s)) Narrative Situation Calculus theories. The following five axioms are taken from [Baker, 1991]. or Holds(F,Result(A,s)) [Holds(Fl,s) ^ ... ^ Holds(Fn,s)] Holds(And(gl,g2),s) --- [Holds(gl,s) AHolds(g2,s)] or Holds(Neg(F),Result(A,s)) Holds(Neg(f+),s)-- ~Holds(f+,s) 032) [Holds(Fl,s) ^ ... ^ Holds(Fn,s)] Holds(g,Sit(g)) 4-- -,Absit(g) 033) whereA is an action constant, F, FI .... Fnare groundfluent Sit(g l)=Sit(g2) -o g 1 034) terms, and for each i and j, l.g.i,j<n, Fi~Neg(Fj). [Holds(f, Result(a,s)) -- Holds(f,s)] 6-- ---,Ab(a,f,s) Definition { Occurrencedescription }. A formula is an Axioms(BI)-(B4) are Baker’s "existence-of-situations" occurrencedescription if it is of the form axioms. Along with the minimisation of Absit, these ensure that all consistent combinations of fluents are Happens(A,T) accounted for in the overall theory. To solve the frame problem, Ab is also minimised (at a lower priority) where A is an action constant and T is a real number.[] allowing Result to vary. For details the reader should consult [Baker, 1991] or [Miller, 1994]. Definition {Narrative domain description}. Given a narrative domainlanguage with positive fluent constants F1 ...... Fn and action constants A1 ...... Am, a formulaN is a narrative domaindescription if it is a conjunctionof I Generalised fluents supply names to conjunctions of action descriptions, initial conditions descriptions, primitive fluents. For example the generalised fluent occurrence descriptions, the frame axiom(F1), existence- "And(Loaded,Neg(Alive))"represents the joint property beingloaded and dead. 2This approach is modified to represent (possibly 3Unlike in [Miller & Shanhan, 1994], domain constraints overlapping) actions with a duration in [Miller & Shanhan, betweenfluents are not consideredhere, since no translation 1994]. of these into logic programswill be given. 1/46 of-situations axioms (B I)-(B4), axioms (NI) and (N2), Example 1 {The Yale Shooting Problem, Nysp]. uniqueness-of-names axiom Holds(Neg(Alive),Result(Shoot,s)) (Y1) UNA[FI...... Fn,And,Neg] ^ UNA[AI...... Am] Holds(Loaded,s) Holds(Loaded,S0) (Y2) and a domainclosure axiomfor fluents Holds(Alive,S0) (Y3) f=F1 v .... v f=Fn v f=Neg(Fl) v .... v f=Neg(Fn ) UNA[Alive,Loaded,And,Neg] CY4) UNA[Sneeze,Shoot] Although domain constraints have not been explicitly (Y5) included in narrative domain descriptions, care must be f=Alive v f=Loaded v CY6) taken, since domain constraints might be derived from f=Neg(Alive) v f=Neg(Loaded) pairs of action descriptions, together with Axiom(B2).
Load more

Recommended publications
  • Probabilistic Event Calculus for Event Recognition
    A Probabilistic Event Calculus for Event Recognition ANASTASIOS SKARLATIDIS1;2, GEORGIOS PALIOURAS1, ALEXANDER ARTIKIS1 and GEORGE A. VOUROS2, 1Institute of Informatics and Telecommunications, NCSR “Demokritos”, 2Department of Digital Systems, University of Piraeus Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this paper, we address the issue of uncertainty in logic-based event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of the Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we study how probabilistic modelling changes the behaviour of the formalism, affecting its key property, the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance. Categories and Subject Descriptors: I.2.3 [Deduction and Theorem Proving]: Uncertainty, “fuzzy,” and probabilistic reasoning; I.2.4 [Knowledge Representation Formalisms and Methods]: Temporal logic; I.2.6 [Learning]: Parameter learning; I.2.10 [Vision and Scene Understanding]: Video analysis General Terms: Complex Event Processing, Event Calculus, Markov Logic Networks Additional Key Words and Phrases: Events, Probabilistic Inference, Machine Learning, Uncertainty ACM Reference Format: Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis and George A.
    [Show full text]
  • Learning Effect Axioms Via Probabilistic Logic Programming
    Learning Effect Axioms via Probabilistic Logic Programming Rolf Schwitter Department of Computing, Macquarie University, Sydney NSW 2109, Australia [email protected] Abstract Events have effects on properties of the world; they initiate or terminate these properties at a given point in time. Reasoning about events and their effects comes naturally to us and appears to be simple, but it is actually quite difficult for a machine to work out the relationships between events and their effects. Traditionally, effect axioms are assumed to be given for a particular domain and are then used for event recognition. We show how we can automatically learn the structure of effect axioms from example interpretations in the form of short dialogue sequences and use the resulting axioms in a probabilistic version of the Event Calculus for query answering. Our approach is novel, since it can deal with uncertainty in the recognition of events as well as with uncertainty in the relationship between events and their effects. The suggested probabilistic Event Calculus dialect directly subsumes the logic-based dialect and can be used for exact as well as a for inexact inference. 1998 ACM Subject Classification D.1.6 Logic Programming Keywords and phrases Effect Axioms, Event Calculus, Event Recognition, Probabilistic Logic Programming, Reasoning under Uncertainty Digital Object Identifier 10.4230/OASIcs.ICLP.2017.8 1 Introduction The Event Calculus [9] is a logic language for representing events and their effects and provides a logical foundation for a number of reasoning tasks [13, 24]. Over the years, different versions of the Event Calculus have been successfully used in various application domains; amongst them for temporal database updates, for robot perception and for natural language understanding [12, 13].
    [Show full text]
  • Mccarthy Variations in a Modal Key ∗ Johan Van Benthem A,B
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Artificial Intelligence 175 (2011) 428–439 Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint McCarthy variations in a modal key ∗ Johan van Benthem a,b, a University of Amsterdam, Institute for Logic, Language and Computation (ILLC), P.O. Box 94242, Amsterdam, GE, Netherlands b Stanford, United States article info abstract Article history: We take a fresh look at some major strands in John McCarthy’s work from a logician’s Available online 3 April 2010 perspective. First, we re-analyze circumscription in dynamic logics of belief change under hard and soft information. Next, we re-analyze the regression method in the Situation Keywords: Calculus in terms of update axioms for dynamic–epistemic temporal logics. Finally, we Circumscription draw some general methodological comparisons between ‘Logical AI’ and practices in Fixed-point logic Structural rules modal logic, pointing at some creative tensions. Belief change © 2010 Elsevier B.V. All rights reserved. Situation Calculus Temporal logic Regression method Dynamic epistemic logic 1. Introduction John McCarthy is a colleague whom I have long respected and admired. He is a truly free spirit who always commu- nicates about issues in an open-minded way, away from beaten academic tracks and entrenched academic ranks. Each encounter has been a pleasure, from being involved with his creative Ph.D. students in Logical AI (each a remarkable char- acter) to his lively presence at our CSLI workshop series on Logic, Language and Computation – and most recently, his participation in the Handbook of the Philosophy of Information [1].
    [Show full text]
  • John Mccarthy – Father of Artificial Intelligence
    Asia Pacific Mathematics Newsletter John McCarthy – Father of Artificial Intelligence V Rajaraman Introduction I first met John McCarthy when he visited IIT, Kanpur, in 1968. During his visit he saw that our computer centre, which I was heading, had two batch processing second generation computers — an IBM 7044/1401 and an IBM 1620, both of them were being used for “production jobs”. IBM 1620 was used primarily to teach programming to all students of IIT and IBM 7044/1401 was used by research students and faculty besides a large number of guest users from several neighbouring universities and research laboratories. There was no interactive computer available for computer science and electrical engineering students to do hardware and software research. McCarthy was a great believer in the power of time-sharing computers. John McCarthy In fact one of his first important contributions was a memo he wrote in 1957 urging the Director of the MIT In this article we summarise the contributions of Computer Centre to modify the IBM 704 into a time- John McCarthy to Computer Science. Among his sharing machine [1]. He later persuaded Digital Equip- contributions are: suggesting that the best method ment Corporation (who made the first mini computers of using computers is in an interactive mode, a mode and the PDP series of computers) to design a mini in which computers become partners of users computer with a time-sharing operating system. enabling them to solve problems. This logically led to the idea of time-sharing of large computers by many users and computing becoming a utility — much like a power utility.
    [Show full text]
  • A Logic-Based Calculus of Events
    10 A Logic-based Calculus of Events ROBERT KOWALSKI and MAREK SERGOT 1 introduction Formal Logic can be used to represent knowledge of many kinds for many purposes. It can be used to formalize programs, program specifications, databases, legislation, and natural language in general. For many such applications of logic a representation of time is necessary. Although there have been several attempts to formalize the notion of time in classical first-order logic, it is still widely believed that classical logic is not adequate for the representation of time and that some form of non-classical Temporal Logic is needed. In this paper, we shall outline a treatment of time, based on the notion of event, formalized in the Horn clause subset of classical logic augmented with negation as failure. The resulting formalization is executable as a logic program. We use the term ‘‘event calculus’’ to relate it to the well-known ‘‘situation calculus’’ (McCarthy and Hayes 1969). The main difference between the two is conceptual: the situation calculus deals with global states whereas the event calculus deals with local events and time periods. Like the event calculus, the situation calculus can be formalized by means of Horn clauses augmented with negation by failure (Kowalski 1979). The main intended applications investigated in this paper are the updating of data- bases and narrative understanding. In order to treat both cases uniformly we have taken the view that an update consists of the addition of new knowledge to a knowledge base. The effect of explicit deletion of information in conventional databases is obtained without deletion by adding new knowledge about the end of the period of time for which the information holds.
    [Show full text]
  • Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming?
    Modeling and Reasoning in Event Calculus Using Goal-Directed Constraint Answer Set Programming? Joaqu´ınArias1;2, Zhuo Chen3, Manuel Carro1;2, and Gopal Gupta3 1 IMDEA Software Institute 2 Universidad Polit´ecnicade Madrid [email protected], [email protected],upm.esg 3 University of Texas at Dallas fzhuo.chen,[email protected] Abstract. Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Cal- culus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of differ- ent inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Pro- gramming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how its expressiveness makes it possible to perform deductive and abductive reasoning tasks in domains featuring, for example, constraints involving dense time and fluents. Keywords: ASP · goal-directed · Event Calculus · Constraints 1 Introduction The ability to model continuous characteristics of the world is essential for Com- monsense Reasoning (CR) in many domains that require dealing with continu- ous change: time, the height of a falling object, the gas level of a car, the water level in a sink, etc. Event Calculus (EC) is a formalism based on many-sorted predicate logic [13, 23] that can represent continuous change and capture the commonsense law of inertia, whose modeling is a pervasive problem in CR.
    [Show full text]
  • Reconciling Situation Calculus and Fluent Calculus
    Reconciling Situation Calculus and Fluent Calculus Stephan Schiffel and Michael Thielscher Department of Computer Science Dresden University of Technology stephan.schiffel,mit @inf.tu-dresden.de { } Abstract between domain descriptions in both calculi. Another ben- efit of a formal relation between the SC and the FC is the The Situation Calculus and the Fluent Calculus are successful possibility to compare extensions made separately for the action formalisms that share many concepts. But until now two calculi, such as concurrency. Furthermore it might even there is no formal relation between the two calculi that would allow to formally analyze the relationship between the two allow to translate current or future extensions made only for approaches as well as between the programming languages one of the calculi to the other. based on them, Golog and FLUX. Furthermore, such a formal Golog is a programming language for intelligent agents relation would allow to combine Golog and FLUX and to ana- that combines elements from classical programming (condi- lyze which of the underlying computation principles is better tionals, loops, etc.) with reasoning about actions. Primitive suited for different classes of programs. We develop a for- statements in Golog programs are actions to be performed mal translation between domain axiomatizations of the Situ- by the agent. Conditional statements in Golog are composed ation Calculus and the Fluent Calculus and present a Fluent of fluents. The execution of a Golog program requires to Calculus semantics for Golog programs. For domains with reason about the effects of the actions the agent performs, deterministic actions our approach allows an automatic trans- lation of Golog domain descriptions and execution of Golog in order to determine the values of fluents when evaluating programs with FLUX.
    [Show full text]
  • Variants of the Event Calculus
    See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/2519366 Variants of the Event Calculus Article · October 2000 Source: CiteSeer CITATIONS READS 42 88 2 authors: Fariba Sadri Robert Kowalski Imperial College London Imperial College London 114 PUBLICATIONS 2,761 CITATIONS 154 PUBLICATIONS 12,446 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Non-modal deontic logic View project SOCS - Societies of Computees View project All content following this page was uploaded by Fariba Sadri on 12 September 2014. The user has requested enhancement of the downloaded file. Variants of the Event Calculus ICLP 95 Fariba Sadri and Robert Kowalski Department of Computing, Imperial College of Science, Technology and Medicine, 180, Queens Gate, London SW7 2BZ [email protected] [email protected] Abstract The event calculus was proposed as a formalism for reasoning about time and events. Through the years, however, a much simpler variant (SEC) of the original calculus (EC) has proved more useful in practice. We argue that EC has the advantage of being more general than SEC, but the disadvantage of being too complex and in some cases erroneous. SEC has the advantage of simplicity, but the disadvantage of being too specialised. This paper has two main objectives. The first is to show the formal relationship between the two calculi. The second is to propose a new variant (NEC) of the event calculus, which is essentially SEC in iff-form augmented with integrity constraints, and to argue that NEC combines the generality of EC with the simplicity of SEC.
    [Show full text]
  • Reconciling the Event Calculus with the Situation Calculus
    1 In The Journal of Logic Programming, Special Issue: reasoning about action and change, 31(1-3), Pages 39-58, 1997. Reconciling the Event Calculus with the Situation Calculus Robert Kowalski and Fariba Sadri Department of Computing, Imperial College, 180 Queen's Gate, London SW7 2BZ Emails: [email protected] [email protected] Abstract In this paper, to compare the situation calculus and event calculus we formulate both as logic programs and prove properties of these by reasoning with their completions augmented with induction. We thus show that the situation calculus and event calculus imply one another. Whereas our derivation of the event calculus from the situation calculus requires the use of induction, our derivation of the situation calculus from the event calculus does not. We also show that in certain concrete applications, such as the missing car example, conclusions that seem to require the use of induction in the situation calculus can be derived without induction in the event calculus. To compare the two calculi, we need to make a number of small modifications to both. As a by-product of these modifications, the resulting calculi can be used to reason about both actual and hypothetical states of affairs, including counterfactual ones. We further show how the core axioms of both calculi can be extended to deal with domain or state constraints and certain types of ramifications. We illustrate this by examples from legislation and the blocks world. Keywords: temporal reasoning, event calculus, situation calculus, actual and hypothetical events 1 Introduction In this paper we revisit our earlier comparison of the situation calculus and event calculus [11] using a more general approach to the representations of situations, in the spirit of Van Belleghem, Denecker and De Schreye [20, 21].
    [Show full text]
  • Actions and Other Events in Situation Calculus
    ACTIONS AND OTHER EVENTS IN SITUATION CALCULUS John McCarthy Computer Science Department Stanford University Stanford, CA 94305 [email protected] http://www-formal.stanford.edu/jmc/ Abstract vention one situation at a time. Then we offer a general viewpoint on the sit- This article presents a situation calculus for- uation calculus and its applications to real malism featuring events as primary and the world problems. It relates the formalism of usual actions as a special case. Events that [MH69] which regards a situation as a snap- are not actions are called internal events and shot of the world to situation calculus theo- actions are called external events. The effects ries involving only a few fluents. of both kinds of events are given by effect axioms of the usual kind. The actions are assumed to be performed by an agent as is 1 Introduction: Actions and other usual in situation calculus. An internal event events e occurs in situations satisfying an occurrence assertion for that event. This article emphasizes the idea that an action by an A formalism involving actions and internal agent is a particular kind of event. The idea of event events describes what happens in the world is primary and an action is a special case. The treat- more naturally than the usual formulations ment is simpler than those regarding events as natural involving only actions supplemented by state actions. constraints. Ours uses only ordinary logic without special causal implications. It also The main features of our treatment are as follows. seems to be more elaboration tolerant.
    [Show full text]
  • Monitoring Business Constraints with the Event Calculus
    Universit`adegli Studi di Bologna DEIS Monitoring Business Constraints with the Event Calculus MARCO MONTALI FABRIZIO M. MAGGI FEDERICO CHESANI PAOLA MELLO WIL M. P. VAN DER AALST June 4, 2012 DEIS Technical Report no. DEIS-LIA-002-11 LIA Series no. 100 Monitoring Business Constraints with the Event Calculus MARCO MONTALI 1 FABRIZIO M. MAGGI 2 FEDERICO CHESANI 3 PAOLA MELLO 3 WIL M. P. VAN DER AALST 2 1Free University of Bozen-Bolzano Research Centre for Knowledge and Data (KRDB) [email protected] 2Eindhoven University of Technology Department of Mathematics & Computer Science ff.m.maggi j [email protected] 3University of Bologna Department of Electronics, Computer Science and Systems (DEIS) ffederico.chesani j [email protected] June 4, 2012 Abstract. Today, large business processes are composed of smaller, au- tonomous, interconnected sub-systems, achieving modularity and robustness. Quite often, these large processes comprise software components as well as hu- man actors, they face highly dynamic environments, and their sub-systems are updated and evolve independently of each other. Due to their dynamic nature and complexity, it might be difficult, if not impossible, to ensure at design-time that such systems will always exhibit the desired/expected behaviours. This in turn trigger the need for runtime verification and monitoring facilities. These are needed to check whether the actual behaviour complies with expected busi- ness constraints. In this work we present mobucon, a novel monitoring framework that tracks streams of events and continuously determines the state of business constraints. In mobucon, business constraints are defined using ConDec, a declarative pro- cess modelling language.
    [Show full text]
  • Situation Calculus
    Institute for Software Technology Situation Calculus Gerald Steinbauer Institute for Software Technology Gerald Steinbauer Situation Calculus - Introduction 1 Institute for Software Technology Organizational Issues • Dates – 05.10.2017 8:45-11:00 (HS i12) lecture and first assignment – 12.10.2017 8:45-11:00 (HS i12) lecture and programming assignment – 11.10.2017 18:00-18:45 (HS i11) practice – 18.10.2017 18:00-18:45 (HS i11) practice and solution for first assignment – 16.10.2017 12:00 (office IST) submission first assignment – 09.11.2016 23:59 (group SVN) submission programming assignment Gerald Steinbauer Situation Calculus - Introduction 2 Institute for Software Technology Agenda • Organizational Issues • Motivation • Introduction – Brief Recap of First Order Logic (if needed) • Situation Calculus (today) – Introduction – Formal Definition – Usage • Programming with Situation Calculus (next week) – Implementation – Domain Modeling Gerald Steinbauer Situation Calculus - Introduction 3 Institute for Software Technology Literature “Knowledge in Action” “Artificial Intelligence: A by Raymond Reiter Modern Approach” MIT Press by Stuart Russel amd Peter Norvig Prentice Hall Gerald Steinbauer Situation Calculus - Introduction 4 Institute for Software Technology Motivation What is Situation Calculus good for? Gerald Steinbauer Situation Calculus - Introduction 5 Institute for Software Technology There is nothing permanent except change. Heraclitus of Ephesus, 535–c. 475 BC Gerald Steinbauer Situation Calculus - Introduction 6 Institute for
    [Show full text]