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Choosing a reasoning style for knowledge based system: lessons from supporting a help desk

Published online by Cambridge University Press:  07 July 2009

Andrew M. Dearden
Affiliation:
Department of Computer Science, University of York, York YOI 5DD, UK
Derek G. Bridge
Affiliation:
Department of Computer Science, University of York, York YOI 5DD, UK

Abstract

In this paper, we present two broad styles of KBS reasoner: those based primarily on some general, explicit model of the knowledge of the domain (whether that model be expressed by heuristic rules or by a deep model of structure and function), which we term domain model-based reasoners; and those based primarily on a set of examples of events in the domain, which we term example-based reasoners (EBR), of which case-based reasoners are a subset. The aim of this paper is to guide developers in considering the trade-offs between these different styles of reasoning. We believe that this cannot be done in general, but may be possible for specific domains. Thus, the paper provides an example analysis of the usefulness of these reasoning styles. We assess the suitability of these styles against a series of requirements which we have identified that KBSs must fulfil if they are to support help desk operations. We conclude that EBR systems are more likely to meet those requirements (the analysis draws on our earlier work in Bridge & Dearden, 1992).

Type
Research Article
Copyright
Copyright © Cambridge University Press 1993

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References

Aaronson, A and Carroll, JM, 1987, “The answer is in the question: a protocol study of intelligent helpBehaviour and Information Technology 6 (4) 393402.CrossRefGoogle Scholar
Abraham, DM, Spangler, WE and May, JH, 1991, “Expertech: Issues in the design and development of an intelligent help desk systemExpert Systems with Applications 2 305319CrossRefGoogle Scholar
Aha, DW, 1991, “Case-based learning algorithms” In: DARPA Case Based Reasoning Workshop pp 147158. Morgan Kaufmann.Google Scholar
Aha, DW, Kibler, D and Albert, MK, 1991, “Instance based learning algorithmsMachine Learning 6 3766.CrossRefGoogle Scholar
Ashley, K and Rissland, E, 1988a. “Waiting on weightings: A symbolic least commitment approach” In: AAAI88, pp 239244. Morgan Kaufmann.Google Scholar
Ashley, KD and Rissland, EL, 1988b, “A case-based approach to modeling legal expertiseIEEE Expert 3 (3) 7077.CrossRefGoogle Scholar
Bain, WM, 1986, Case-Based Reasoning: A Computer Model of Subjective Assessment PhD thesis, Yale University.Google Scholar
Bareiss, R (ed), 1991, DARPA Case Based Reasoning Workshop 1991 Morgan Kaufmann.Google Scholar
Bareiss, ER, Porter, BW and Wier, CC, 1991, “Protos: an exemplar-based learning apprentice” In: Kodratoff, Y and Michalski, R (eds.), Machine Learning: An Artificial Intelligence Approach Vol 3, pp 112127. Morgan Kaufmann.Google Scholar
Bareiss, R (chair), 1989, “Panel discussion of indexing vocabulary” In: DARPA Case based reasoning workshop 1989, pp 6684. Morgan Kaufmann.Google Scholar
Baroff, J, Simon, R, Gilman, F, and Shneiderman, B, 1988, “Direct manipulation user interfaces for expert systems” In: Hendler, J (ed.), Expert Systems: The User Interface, pp 99125. Ablex.Google Scholar
Barr, A, 1991, “Transforming support at the help desk” Information Centre Quarterly Spring 2027.Google Scholar
Brachman, RJ, 1985, “On the epistemological status of semantic networks”. In: Brachman, RJ and Levesque, HJ (eds.), Readings in Knowledge Representation, pp 191216, Morgan Kaufmann.Google Scholar
Brachman, RJ and Levesque, HJ (eds.), 1985, Readings in Knowledge Representation Morgan Kaufmann.Google Scholar
Breuker, J and Wielinga, B, 1987, “Use of models in the interpretation of verbal data” In: AL, Kidd (ed.), Knowledge Acquisition for Expert Systems. Plenum Press.Google Scholar
Bridge, DG and Dearden, AM, 1992, “Knowledge based systems support for help desk operations: A reference modelInt. J. Systems Research and Information Science 5 217234.Google Scholar
Carbonnel, JG, 1986, “Derivational analogy: A theory of reconstructive problem solving and expertise acquisition” In: Michalski, RS, Carbonell, JG and Mitchell, TM (eds.), Machine Learning: An Artificial Intelligence Approach, Volume II, pp 371392. Morgan Kaufmann.Google Scholar
Chandrasekeran, B, 1983, “Towards a taxonomy of problem solving types” The Al Magazine Winter/Spring 917.Google Scholar
Clancey, Wi, 1985, “Heuristic classificationArtificial Intelligence 27 289350.CrossRefGoogle Scholar
Clancey, Wi, 1986, “From Guidon to Neomycin to Heracles in twenty short lessons: Orn final report 1975–1985” The Al Magazine 08 4060, 187.Google Scholar
Clancey, WJ and Letsinger, R, 1981, “Neomycin: Reconfiguring a rule based expert system for applications to teaching” In: Proc. IJCAI81, pp 829836.Google Scholar
Coombs, MJ and Alty, JL, 1980, “Face to face guidance of university computer users 2. Characterising advisory interactionsInt J. Man Machine Studies 12 389405.CrossRefGoogle Scholar
Dearden, AM, 1993, “Interacting with a case memory” In: Proc IEE Colloquium on Case-Based Reasoning. IEE, Computing and Control Division, Professional Group C4 (Artificial Intelligence).Google Scholar
Debenham, J, 1989, “The normalized model and expert systems maintenance” In: Shadbolt, N (ed.), Research and Development in Expert Systems VI, pp 7888. Cambride University Press.Google Scholar
DeKleer, J, 1986, “An assumption-based TMSArtificial Intelligence 28 127162.CrossRefGoogle Scholar
Duda, R, Gasching, J and Hart, P, 1979, “Model design in the PROSPECTOR consultant system for mineral exploration” In: Michie, D (ed.), Expert Systems in the Microelectronic Age, pp 153167. Edinburgh University Press.Google Scholar
Fischer, G, 1990, “Communications requirements for co-operative problem solving systemsInformation Systems 15 (1) 2136.CrossRefGoogle Scholar
Fox, J, Clarke, DA, Glowinski, AJ and O'Neil, MJ, 1990, “Using predicate logic to integrate qualitative reasoning and classical decision theoryIEEE Trans. on Systems, Man and Cybernetics 20 (2) 347357.Google Scholar
Green, TRG, 1989, “Cognitive Dimensions of notations” In: Sutcliffe, A and Macaulay, L (eds.) People and Computers V, Proceedings of HCI 89, pp 443460, Cambridge University Press.Google Scholar
Greiner, R, 1988, “Learning by understanding analogies” In: Prieditis, A (ed.), Analogica, pp 136. Pitman.Google Scholar
Hammond, K, 1989a, Case Based Planning Academic Press.CrossRefGoogle Scholar
Hammond, K (ed.), 1989b, DARPA Case Based Reasoning Workshop 1989 Morgan Kaufmann.Google Scholar
Hennessy, D and Hinkle, D, 1992, “Applying case based reasoning to autoclave loading” IEEE Expert 10 2126.CrossRefGoogle Scholar
Hickman, FR, Killin, JL, Land, L, Mulhall, T, Porter, D and Taylor, RM, 1989, Analysis for Knowledge Based Systems a practical guide to the KADS methodology. Ellis Horwood.Google Scholar
Hinrichs, TR and Kolodner, JL, 1991, “The roles of adaptation in case based design” In: AAAI 91, pp 2833. MIT Press.Google Scholar
Inder, R, 1989a, “Experience of constructing a fault localisation expert system using an AI toolkit” In: Vadera, S (ed), Expert System Applications, pp 151167. Sigma Press.Google Scholar
Inder, R, 1989b, “The state of the art” In: Vadera, S (ed.), Expert Systems Applications, pp 151167. Sigma Press.Google Scholar
Johnson, L, 1985, “The MDX diagnostic systemInt. J. Systems Research and Info. Science 1 163171.Google Scholar
Johnson, L and Ghalal, GH, 1992, “Methodological issues in knowledge based systems development” In: IEE Colloquium on Software Engineering and AI.Google Scholar
Johnson, L and Keravnou, ET, 1988, Expert Systems Architectures. Kogan Page, 2nd edition.Google Scholar
Jones, EK, 1989. “Case-based analogical reasoning using proverbs” In: Hammond, K (ed), Proceedings: Case Based Reasoning Workshop, pp 275279. Morgan Kaufmann.Google Scholar
Kedar-Cabelli, ST, 1988, Formulating concepts and Analogies According to Purpose. PhD thesis, Rutgers University.Google Scholar
Keravnou, ET and Johnson, L, 1986, Competent Expert Systems. Kogan Page.Google Scholar
Keravnou, ET and Washbrook, J, 1989, “What is a deep expert system: An analysis of the architectural requirements of second-generation expert systems. Knowledge Engineering Review 4 (3) 205233.CrossRefGoogle Scholar
Keravnou, ET, Washbrook, J, Dawood, RM, Hall, CM and Shaw, D, 1989, “A model based diagnostic expert system for skeletal dysplasias”. In: J Hunter, J Cookson and J Wyatt (eds.), AIME89: Proceedings of the second European Conference on Artificial Intelligence in Medicine, pp 4756.CrossRefGoogle Scholar
Kibler, D and Aha, DW, 1988, “Case based classification” In: AAAI88 Case Based Reasoning Workshop, pp. 6267.Google Scholar
Kidd, AL, 1985, “What do users ask? Some thoughts on diagnostic advice” In: Proceedings of the 5th Technical Conference of the BCS Special Interest Group on Expert Systems, pp 919.Google Scholar
Kilhoffer, AR and Wisely, CL, 1990, “HELDA: The help desk assistant”, Al Expert February 5759.Google Scholar
Kolodner, JL (ed), 1988. DARPA Case Based Reasoning Workshop1988. Morgan Kaufmann.Google Scholar
Land, L and Mulhall, T, 1989, “Developing co-operative knowledge based systems” In: Shadbolt, N (ed.), Research and Development in Expert Systems VI, pp 90103. Cambridge University Press.Google Scholar
Laufmann, SC, DeVaney, DM and Whiting, MA, 1990, A methodology for evaluating potential KBS applications. IEEE Expert 12 4362.CrossRefGoogle Scholar
Maybury, MT, 1990, Planning Multisentential English Text Using Communicative Acts. Technical Report RADC-TR-90–411, Rome Air Development Center, Griffiss Air Force Base, NY.Google Scholar
Minsky, M, 1985, “A framework for representing knowledge” In: Brachman, RJ and Levesque, HJ (eds.), Readings in Knowledge Representation, pp 245263. Morgan Kaufmann.Google Scholar
Motro, A, 1987, “Extending the relational database model to support goal queries” In: Kerschberg, L (ed.), Expert Database Systems, pp 129150. Benjamin/Cummings.Google Scholar
Muns, RJ, 1990, “The expert help desk.. 28 factors for success” In: Conference on Artificial Intelligence and the Help Desk. The Help Desk Institute.Google Scholar
Neches, R, Swartout, WR and Moore, JD, 1985, “Enhanced maintenance and explanation of expert systems through explicit models of their development”. IEEE Transactions of Software Engineering 11.Google Scholar
Olson, G, Sheppard, S and Soloway, E (eds.), 1987, Empirical Studies of Programmers. Ablex.Google Scholar
Patil, RS, Szolovits, P and Schwarz, WB, 1981, “Causal understanding of patient illness in medical diagnosis” In: Procs IJCAI81, pp 893899.Google Scholar
Pearce, M, Goel, AK, Kolodner, JL, Zimring, C, Sentosa, L and Billington, R, 1992, “Case based design support: A case study in architectural design”, IEEE Expert, 10, pp. 1420.CrossRefGoogle Scholar
Pollack, ME, 1985, “Information sought and information provided: An empirical study of user/expert dialogues” In: Proceedings of the ACM/CHI San Fransisco, pp 155159.CrossRefGoogle Scholar
Pople, H Jr, 1985, “Evolution of an expert system: from Internist to Caduceus” In: DeLotto, I and Stefanelli, M (eds.), Artificial Intelligence in Medicine. Elsevier.Google Scholar
Prieditis, A (ed), 1988, Analogica. Pitman.Google Scholar
Rajamoney, SA and Lee, H-Y, 1991. “Prototype based reasoning: An integrated approach to solving large novel problems” In: AAAI 91 Transformation in Design Workshop, pp 3439. AAAI/MIT Press.Google Scholar
Register, MS and Rewari, A, 1991, “CANASTA: The crash analysis troubleshooting assistant” In: Smith, RG and Scott, AC (eds.), Innovative Applications of Artificial Intelligence 3, pp 195212.Google Scholar
Seifert, C (chair), 1989, “Panel on analogy and CBR” In: Hammond, K (ed.), Proceedings: Case Based Reasoning Workshop 1989, pp 125159. Morgan Kaufmann.Google Scholar
Shafer, D, 1991, “CBR Express: Getting down to cases”. PC Al 07/08 4245.Google Scholar
Sholtz, J and Wiedenbeck, S, 1992. “The use of unfamiliar programming languages by experienced programmers” In: Monk, A, Diaper, D and Harrison, MD (eds.), People and Computers VII, pp 4556. Cambridge University Press.Google Scholar
Shortliffe, EH, 1976, Computer-based Medical Consultations. Elsevier.Google Scholar
Shum, S, 1991. “Cognitive dimensions of design rationale” In: Diaper, D and Hammond, N (eds.), People and Computers VI, pp 331344. Cambridge University Press.Google Scholar
Simoudis, E and Miller, JS, 1991, “The application of CBR to help desk applications” In: DARPA Case Based Reasoning Workshop 1991. Morgan Kaufmann.Google Scholar
Soloway, E and Iyengar, S (eds.), 1986, Empirical Studies of Programmers. Ablex.Google Scholar
Steels, L, 1986, “Second-generation expert systems” In: Bramer, M (ed), Research and Development in Expert Systems III. Cambridge University Press.Google Scholar
Stenton, SP, 1987, “Dialogue management for co-operative knowledge based systems”. Knowledge Engineering Review 2 (2) 99122.CrossRefGoogle Scholar
Swaminathan, K, 1988. “Properties of an indexing scheme” In: AAA1 Case Based Reasoning Workshop, pp. 132136.Google Scholar
Swartout, WR and Smoliar, SW, 1987, “On making expert systems more like experts”. Expert Systems 4 (3).CrossRefGoogle Scholar
Taylor, JM, 1985. “An expert system for terminal fault diagnosis” In: First International Expert Systems Conference, pp 193199.Google Scholar
Thompson, K and Langley, P, 1989, “Organisation and retrieval of composite concepts” In: Kolodner, JL (ed), DARPA Case Based Reasoning Workshop 1989, pp 329333. Morgan Kaufmann.Google Scholar
Travé-Massueyès, L et al. , 1992, “Qualitative reasoning”. Special Issue of Knowledge Engineering Review 7 (1).Google Scholar
Turner, RM, 1989, “Case-based and Schema-based reasoning for problem solving” In: Proceedings of the Case-Based Reasoning Workshop, pp 341344. Morgan Kaufmann.Google Scholar
Tyree, AL, Greenleaf, G and Mowbray, A, 1988, “Legal reasoning: The problem of precedent” In: Gero, JS and Stanton, R (eds.), Artificial Intelligence Developments and Applications, pp 231245. Elsevier.Google Scholar
Ward, RD and Sleerman, D, 1987, “Learning to use the S.1 knowledge engineering toolKnowledge Engineering Review 2 265276.CrossRefGoogle Scholar
Weiss, SM, Kulikowski, CA, Amarel, S and Safir, A, 1978, “A model-based method for computer-aided medical decision-making”. Artificial Intelligence 11 145172.CrossRefGoogle Scholar
Wiedenbeck, S, 1986. “Processes in computer program comprehension” In: Soloway, E and Iyenagar, S (eds.), Empirica Studies of Programmers, pp 5879. Ablex.Google Scholar
Young, RM, 1989, “Human interface aspects of expert systems” In: Murray, LA and Richardson, JTE (eds.), Intelligent Systems in a Human Context, pp 2034. Oxford University Press.Google Scholar
Zadeh, LA, 1965, “The role of fuzzy logic in the management of uncertainty in expert systems”. Fuzzy Sets and Systems 11 199227.CrossRefGoogle Scholar