A Constraint-Based Approach to the Description and Detection Of

Linkoping Electronic Articles in Computer and Information Science Vol nr A ConstraintBased Approach to the Description and Detection of FitnessforPurp ose Simon White and Derek Sleeman Linkoping University Electronic Press Linkoping Sweden httpwwwepliuseeacis PublishedonDecember by Linkoping University Electronic Press Linkoping Sweden Linkoping Electronic Articles in Computer and Information Science ISSN Series editor Erik Sandewal l c Simon White and Derek Sleeman A Typeset by the author using L T X E Formattedusingetendu style Recommended citation Author Title Linkoping Electronic Articles in Computer and Information Science Vol nr httpwwwepliuseeacisDecember This URL wil l also contain a link to the authors home page The publishers wil l keep this article online on the Internet or its possible replacement network in the future for a periodofyears from the date of publication barring exceptional circumstances as describedseparately The online availability of the article implies 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anceofdocument integrity please refer to its WWW home page httpwwwepliuse or by conventional mail to the address statedabove Abstract This pap er intro duces the notion of tnessforpurpose presents a tractable approximate approach to the recognition of tness forpurp ose and describ es a working implementation using con straint programming The prop erty of tnessforpurpose states whether running a software comp onent with a supplied set of inputs can satisfy a given goal Our interest is to assess whether a chosen problem solver together with one or more knowledge bases can satisfy a given problemsolving goal In general this is an intractable problem We therefore intro duce an eective practical approxi to tnessforpurp ose based on the plausibility of the goal mation Webelieve that constraint logic programming provides a natu ral approach to the implementation of such approximations We to ok the Common LISP constraints library SCREAMER and extended its symb olic capabilities to suit our purp oses Authors address Department of Computing Science Kings College University of Ab erdeen Ab erdeen AB UE Scotland UK Email hswhite dsleemanicsdabdnacuk Intro duction The prop ertyof tnessforpurpose states whether running a software com ponent with a supplied set of inputs can satisfy a given goal Fitnessfor purp ose is related to the notion of comp etence Wielinga Akkermans Schreib er but with some imp ortant dierences Firstly whilst com p etence describ es the general inputoutput relationship of a problemsolver the verication of tnessforpurp ose involves a test of a problem solver and a sp ecic set of input instances against a given goal Secondly tnessfor purp ose is a descriptive notion b ecause it is applied to an existing system whereas comp etence is a prescriptive term applied to a system that is to b e built Descriptions of tnessforpurp ose can enable human or machine agents to assess the suitability of application of the describ ed comp onent for some particular task Some established approaches to assessing whether a soft ware comp onent is suitable for solving the task at hand are by design test ing verication validation proving properties and syntacticstructural matching with requirements Suitability by design aims to develop new com ponents to satisfy requirements Suitability by testing aims to detect faults in existing comp onents by careful preparation of test case inputs Suitability by verication validation aims to check whether knowledgebased sys tems meet their users requirements by for example identifying redundant or conicting rules in a knowledge base Suitability by proving properties is p otentially more informative However when done by hand it is a dif cult and involved pro cess and only feasible for small programs When automated it can also b e problematic for example MacKenzie notes that wholly automatic theorem provers have so far b een considered inadequate to these tasks MacKenzie The semiautomatic assessment of tnessforpurp ose is b ecoming in creasingly imp ortant for two main reasons Firstly as the range and sophis tication of software increases it b ecomes dicult for a human user to make an informed choice of the b est software solution for any particular task The same p oint applies equally to domain indep endent reasoning comp onents such as the problem solving metho ds PSMs Benjamins Fensel We b elieve that a novice user of such comp onents in particular could b ene t greatly from advice generated as a result of a tnessforpurp ose analysis Secondlywe observe a demand for software brokers which given some soft ware requirements return either the software itself or a reference to such software In the knowledge acquisition community the Ibrow pro ject Benjamins et al intends to build such a broker for the distribution of problem solving comp onents Recent work on the description of comp etence includes that of Ben jamins et al in the context of the Ibrow pro ject Benjamins et al Benjamins et al Fensel et al as part of an ongoing investigation into the role of assumptions Fensel Schonegge Fensel Schonegge and Wielinga et al as a metho dological approach to the op erational isation of knowledgelevel sp ecications Wielinga Akkermans Schreib er We consider Fensels approach to b e the nearest to our work b ecause it investigates the context dependency of an existing problem solverPSM through the discovery of assumptions Our work also investigates the con text dep endency of problem solvers but through the question of task suit ability with the available knowledge Thus whilst Fensel investigates a problem solving metho d in isolation of its inputs in order to derive suitabil ity conditions we take a problem solver together with its inputs and test Figure Comparison of the Fensel approach i and our approach ii to the discovery of problem solving prop erties their combined suitability for solving a sp ecic task Both lines of inquiry are intractable and therefore demand some compromises in any imple mentation Fensels compromise concerns the level of automation of his pro of mechanism whichisaninteractive verier rather than a fully auto mated pro of mechanism Since wewould like to generate advice at runtime for the p otential user of a problem solver we compromise instead with the deductivepower of our pro of mechanism We b elieve however that what our constraint satisfaction mechanism may lack in expressivepower it gains in its abilities to combine the results of multiple problem solvers through propagation mechanisms and to run as a batch pro cess Figure compares Fensel et als pro cess of assumption hunting with our approach to matching a problem solver to a to olkit users goal Our approach to tnessforpurp ose analysis was develop ed to assist in the generation of advice for novice users of knowledge acquisition to ols KA to ols and problemsolvers in the Muskrat system Graner Slee man Sleeman White White Sleeman White Muskrat is a MUltiStrategy Knowledge Renement and Acquisition To olb ox that makes it easier for novice users to apply and combine the incorp orated software to ols to solve some sp ecic task When generating advice on the application of a chosen problemsolver Muskrat should b e able to dierentiate between the following three cases for the knowledge bases available to the problemsolver Case The problemsolver can be applied with the knowledge bases already available ie no acquisition or mo dication of knowledge is neces sary Case The problemsolver needs knowledge bases not currently available therefore these knowledge bases must b e rst acquired Case The problemsolver needs knowledge bases not currently available but the requirements can be met by mo difying existing knowl edge bases The phrase tnessforpurp ose and the computational approximation to it as describ ed in this pap er represents our approach to case We exp ect subsequent researchtoinvestigate issues relating to cases and In the next section we dene more exactly what wemeanby tness forpurp ose and explain the computational diculties which arise in its analysis In section we describ e how we approach these diculties by considering an approximation of tnessforpurp ose rather than the actual tnessforpurp ose In section we explain howwe are implementing these ideas using constraint logic programming in Common Lisp Finally in section we summarise the ideas relate them to other work in the eld and indicate some p ossible directions for future work FitnessforPurp ose When the advisory subsystem of Muskrat addresses the problem of tness

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