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Case-based reasoning: A review

Published online by Cambridge University Press:  07 July 2009

Ian Watson  and
Farhi Marir
Ian Watson
Affiliation:
Department of Surveying, University of Salford, Salford MS 4WT, UK
Farhi Marir
Affiliation:
Department of Surveying, University of Salford, Salford MS 4WT, UK
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Abstract

Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems.1 A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).

Type
Research Article
Information
The Knowledge Engineering Review , Volume 9 , Issue 4 , December 1994 , pp. 327 - 354
Copyright
Copyright © Cambridge University Press 1994

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References

Aamodt, A, 1989. “Towards robust expert systems that learn from experience—an architectural framework”. In: Boose, J, Gaines, B, Ganascia, J-G (eds.), EKAW-89; Third European Knowledge Acquisition for Knowledge-Based Systems Workshop, pp 311–326.Google Scholar
Aamodt, A, 1991. A knowledge intensive approach to problem solving and sustained learning. Ph.D dissertation, University of Trondheim, Norwegian Institute of Technology, 05.Google Scholar
Aamodt, A and Plaza, E, 1994. “Case-based reasoning: foundational issues, methodological variations, and system approaches”. AI Communications 7 (1) 39–59.CrossRefGoogle Scholar
Acorn, T and Walden, S, 1992. “SMART: Support management cultivated reasoning technology Compaq customer service”. In: Proceedings of AAAI-92. AAAI Press/MIT Press.Google Scholar
Alexander, JH, Freiling, MJ, Shulman, SJ, Staley, JL, Rehfuss, S and Messick, SL, 1986. “Knowedge level engineering: ontological analysis”. AAAI-86 21 963–968.Google Scholar
Alterman, R, 1986. “An adaptive planner”. In: Proceedings of AAAI-86. AAAI Press/MIT Press.Google Scholar
Alterman, R, 1988. “Adaptive planning”. Cognitive Science 12 393–422.CrossRefGoogle Scholar
Alterman, R, 1989. “Panel discussion on case representation”. In: Proceedings Second Workshop on Case- Based Reasoning. Pensacola Beach, FL.Google Scholar
Althoff, KD, 1989. “Knowledge acquisition in the domain of CBC machine centres: the MOLTKE approach”. In: EKAW-89, Third European Workshop on Knowledge-Based Systems, Boos, J, Gaines, B and Ganascia, JG (eds.), pp 180–195.Google Scholar
Aleven, V and Ashley, KD, 1992. “Automated generation of examples for a tutorial in case-based argumentation”. In: Frasson, C, Gauthier, G and GI, McCallan (eds.), Proceedings Second International Conference of intelligent Tutoring Systems (1TS92). Springer-Verslag.Google Scholar
Ashley, KD, 1988. “Arguing by analogy in law: a case-based model”. In: Helman, DH (Ed.), Analogical Reasoning: Perspectives of Artificial Intelligence, Cognitive Science, and Philosophy. Reidel.Google Scholar
Bachant, J and McDermott, J, 1984. “R1revisited: four years in the trenches”. The AI Magazine 5 (3).Google Scholar
Bain, WM, 1986. Case-based reasoning: a computer model for subjective assessment. Ph.D thesis, Yale University.Google Scholar
Bareiss, ER, 1988. PROTOS: A unified approach to concept representation, classification, and learning. Ph.D thesis, Department of Computer Science, University of Texas.Google Scholar
Barletta, R and Mark, W, 1988. “Explanation-based indexing of cases”. In: Proceedings Seventh National Conference on Artificial Intelligence. Minneapolis, MN.Google Scholar
Barletta, R, 1991. “An introduction to case-based reasoning”. AI Expert 08, 42–49.Google Scholar
Bench-Capon, TJM and Coenen, F, 1992. “The maintenance of legal knowledge based systems”. Al Review 6 129–143.Google Scholar
Birnbaum, L and Collings, G. 1989. “Remindings and engineering design themes: a case study in indexing vocabulary”. In: Proceedings of Second Workshop on Base-Based Reasoning. Pensacola Beach, FL.Google Scholar
Boose, JH, 1989. “A survey of knowledge acquisition techniques and tools”. Knowledge Acquisition 1(1).CrossRefGoogle Scholar
Branting, K, 1991. “Exploiting the complementarity of rules and precedents with reciprocity and fairness.” In: Proceedings of the Case-Based Reasoning Workshop 1991, Washington, DC, 05. (Sponsored by DARP).Google Scholar
Brooke, S and Jackson, C, 1991. “Advances in elicitation by exception”. In: Proc. 1st SGES mt. Workshop on Knowledge Based Systems Methodologies. pp 70–78, British Computer Society SGES.Google Scholar
Burton, AM, Shabolt, NR, Rugg, G and Hedgecock, AP, 1988. “Knowledge elicitation techniques in classification domains”. In: Proceedings ECAl-88: The 8th European Conference on Artificial Intelligence.Google Scholar
Chandrasekaran, B, 1986. “Generic tasks in knowledge-based reasoning: high level building blocks for expert system design”. IEEE Expert 1 23–30.CrossRefGoogle Scholar
Chandrasekaran, B, 1990. “Design problem solving: a task analysis”. AI Magazine Winter, 59–73.Google Scholar
Clancey, WJ, 1985. “Heuristic classification”. Artificial Intelligence 27 289–350.CrossRefGoogle Scholar
Coenen, F and Bench-Capon, TJM, 1992. “Maintenance and maintainability in regulation based systems”. ICL Technical Journal 05, 76–84.Google Scholar
Collins, G, 1987. Plan creation: using strategies as blueprints. Ph.D Thesis, Department of Computer Science, Yale University.Google Scholar
Costas, T and Kashyap, , 1993. “Case-based reasoning and learning in manufacturing with TOTLEC planner”. IEEE Transactions on Systems, Man, and Cynernetics 23.Google Scholar
David, BS, 1991. “Principles for case representation in a case-based aiding system for lesson planning”. In: Proceedings Workshop on Case-Based Reasoning Washington, DC, 8–1005.Google Scholar
Dearden, AM and Bridge, DG, 1993. “Choosing a reasoning style for a knowledge-based system: lessons from supporting a help desk”. The Knowledge Engineering Review 8 210–222.CrossRefGoogle Scholar
Diaper, D. 1989. Knowledge Elicitation: Principles, Techniques and Applications. Ellis Horwood.Google Scholar
Domeshek, E, 1993. “A case study of case indexing: designing index feature sets to suit task demands and support parallelism”. In: Barenden, J and Holyoak, K (eds.), Advances in Connectionnist and Neural Computation Theory, Vol. 2: Analogical Connections.Google Scholar
DTI, 1992. Knowledge-Based Systems Survey of UK Applications. Department of Trade & Industry, UK.Google Scholar
Falkeneheimer, B, Forbus, KD and Gentner, D, 1986. “The structure mapping engine”. In: Proceedings Sixth National Conference on Artificial Intelligence, Philadelphia, PA.Google Scholar
Farrel, R, 1987. “Intelligent case selection and presentation”. In: Proceedings Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 1 174–176.Google Scholar
Gallaire, H, Minker, J and Nicolas, JM, 1981. Advances in Database Theory, Vol. 1, Plenum.Google Scholar
Gentner, D, 1983. “Structure mapping—a theoretical framework for analogy”. Cognitive Science 7 155–170.Google Scholar
Goodman, M, 1989. “CBR in battle planning”. In: Proceedings Second Workshop on Case-Based Reasoning, Pensacola Beach, FL.Google Scholar
Hammond, KJ, 1986. “CHEF: A model of case-based planning”. In: Proc. American Association for Artificial Intelligence, AAAI-86, Philadelphia, PA.Google Scholar
Hammond, KJ, 1987. “Explaining and repairing plans that fail”. In: Proceedings International Joint Conferences on Artificial Intelligence, 1JCAI-87, Milan, Italy.Google Scholar
Hammond, KJ, 1989. “On functionally motivated vocabularies: an apologia”. In: Proceedings Second Workshop on Case-Based Reasoning, Pensacola Beach, FL.Google Scholar
Harmon, P, 1992. “Case-based reasoning III”. Intelligent Software Strategies 8 (1).Google Scholar
Hayes-Roth, F, Waterman, D and Lenat, D (eds.) 1983. Building Expert Systems. Addison Wesley.Google Scholar
Helton, T, 1991. “The hottest new AI technology—case-based reasoning”. The Spang Robinson Report on Artificial Intelligence 7(8).Google Scholar
Hennessy, D and Hinkle, D, 1992. “Applying case-based reasoning to autoclave loading”. IEEE Expert 7(5) 21–26.CrossRefGoogle Scholar
Hinrichs, TR, 1992. Problem Solving in Open Worlds. Lawrence Erlbaum.Google Scholar
Kass, A, 1986. “Modifying explanations to understand stories”. In: Proc. 8th. Annual Conf. of the Cognitive Science Society. Lawrence Erlbaum.Google Scholar
Keane, M, 1988. “Where's the beef? The absence of pragmatic factors in theories of analogy”. In: ECAI-88 pp 327–332.Google Scholar
Kitano, H, 1993. “Challenges for massive parallelism”. In: Proc. 13th. Conference on Artificial Intelligence, IJCAI-93, pp 813–834.Google Scholar
Kolodner, JL, 1993a. “Maintaining organization in a dynamic long-term memory”. Cognitive Science 7(4) 243–280.Google Scholar
Kolodner, JL, 1983b. “Reconstructive memory: a computer model”. Cognitive Science 7(4) 281–228.Google Scholar
Kolodner, JL, 1993. Case-Based Reasoning. Morgan-Kaufmann.CrossRefGoogle Scholar
Koton, P, 1989. Using experience in learning and problem solving. Ph.D thesis, Massachusetts Institute of Technology, Laboratory of Computer Science.Google Scholar
Leake, DB, 1994. “Case-based reasoning”. The Knowledge Engineering Review 9(1) 61–64.CrossRefGoogle Scholar
Lebowitz, M, 1987. “Experimental with incremental concept information: UNIMEM”. Machine Learning 2(2) 103–138.CrossRefGoogle Scholar
Lopez, B and Plaza, E, 1993. “Case-based planning for medical diagnosis”. Methodologies for Intelligent Systems, 7th. International Symposium, ISMIS-93: Lecture Notes in Artificial Intelligence 689. Springer- Verlag.Google Scholar
Magaldi, RV, 1994. “CBR for troubleshooting aircraft on the flightline”. In: Proc. lEE Colloquium on Case- Based Reasoning: Prospects for Applications. Digest No: 1994/057, pp 6/1–6/9.Google Scholar
Maher, ML and Zhang, DM, 1991. “CADSYN: using case and decomposition knowledge for design synthesis”. In: Gero, JS (ed.), Artificial Intelligence in Design. Butterworth-Heinmann.Google Scholar
Marir, F and Yip, YJ, 1992. “An inference system based on the compiled approach for the relational database systems”. ITI Papers of the University of Salford, pp 47–72, 05.Google Scholar
Marir, F, 1993. An integration approach for the deductive database systems: Enhancing the relational database system with a logic inference based on a compiled approach. Ph.D Thesis, University of Salford.Google Scholar
Marir, F and Watson, ID, 1994. “Case-based reasoning: a categorized bibliography”. The Knowledge Engineering Review 9(4).CrossRefGoogle Scholar
Milne, R and Nelson, C, 1994. “Knowledge guided data mining”. In: Proc. lEE Colloquium on Case-Based Reasoning: Prospects for Applications. Digest No: 1994/057, pp 10/1–10/3.Google Scholar
Minker, J, 1988. “Perspectives in deductive databases”. Journal of Logic Programming 5 33–60.CrossRefGoogle Scholar
Moore, CJ, Lehane, MS and Proce, CJ, 1994. “Case-based reasoning for decision support in engineering design”. In: Proc. lEE Colloquium on Case-Based Reasoning: Prospects for Applications. Digest No: 1994/057, pp 4/1–4/4.Google Scholar
Moorman, K and Ram, A, 1992. “A case-based approach to reactive control for autonomous robots”. In: Proceedings AAAI Fall Symposium on AI for Real-World Autonomous Robots. AAAI Press/MIT Press.Google Scholar
Motta, E, Rajan, T and Eisenstadt, M, 1989. “A methodology and tool for knowledge acquisition in KEATS- 2”. In: Guida, G and Tasso, C (eds.), Topics in Expert System Design: Methodologies and Tools pp 297–322. North-Holland.CrossRefGoogle Scholar
Navinchandra, D, 1991. Exploration and Innovation in Design: Towards a Computational Model. Springer- Verlag.CrossRefGoogle Scholar
Owens, C, 1993. “Integrating feature extraction and memory search”. Machine Learning 10(3) 311–340.CrossRefGoogle Scholar
Oxman, RE, 1993a. “PRECEDENTS: Memory structure in design case libraries”. In: CAAD Futures 93. Elsevier.Google Scholar
Oxman, RE, 1993b. “Case-based design support: Supporting architectural composition through precedent libraries”. Journal of Architectural Planning Research.Google Scholar
Pearce, M, Ashok, KG, Kolodner, JL, Zimring, C and Billington, R, 1992. “Case-based support—A case study in architectural design”. IEEE Expert 10.CrossRefGoogle Scholar
Porter, BW and Bareiss, ER, 1986. “PROTOS: An experiment in knowledge acquisition for heuristic classification tasks”. In: Proceedings First International Meeting on Advances in Learning (IMA L), pp 159–174, Les Arcs, France.Google Scholar
Quinlan, JR, 1979. Induction over large databases. Rep. No. HPP-79–14, Heuristic Programming Project, Computer Science Dept., Stanford University.Google Scholar
Ram, A, Arkin, RC, Moorman, K and Clark, RJ, 1994. Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters in autonomous robotic systems. Georgia Institute of Technology, College of Computing Technical report no. GIT-CC-92/57.Google Scholar
Richter, AM and Weiss, S, 1991. “Similarity, uncertainty and case-based reasoning in PATDEX”. In: RS, Boyer (ed.), Automated Reasoning, Essays in Honour of Woody Bledsoe. Kluwer pp 249–265.CrossRefGoogle Scholar
Reisbeck, CK and Schank, RC 1989. Inside Case-Based Reasoning. Lawrence Erlbaum.Google Scholar
Rissland, EL and Skala, DB, 1989. “Combining case-based and rule-based reasoning: A heuristic approach”. In: Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89, pp 524–530, Detroit.Google Scholar
Schank, RC and Abelson, RP, 1977. Scripts, Plans, Goals and Understanding. Erlbaum.Google Scholar
Schank, R, 1982. Dynamic Memory: A theory of reminding and learning in computers and people. Cambridge University Press.Google Scholar
Sharma, S and Sleeman, D, 1988. “REFINER: A case-based differential diagnosis aide for knowledge acquisition and knowledge refinement”. In: EWSL 88; Proc. European Working Session on Learning pp 201–210.Google Scholar
Simoudis, E, 1992. “Using case-based retrieval for customer technical support”. IEEE Expert 7(5) 7–13.CrossRefGoogle Scholar
Simoudis, E, Mendall, A and Miller, P, 1993. “Automated support for developing retrieve-and-propose systems”. In: Proceedings Artificial Intelligence XI Conference, Orlando, FL.Google Scholar
Simpson, RL, 1985. A Computer Model of Case-Based Reasoning in Problem Solving: An Investigation in the Domain of Dispute Mediation. Technical Report GIT-ICS-85/18, Georgia Institute of Technology, School of Information and Computer Science.Google Scholar
Skalk, DB, 1992. “Representing cases as knowledge sources that apply local similarity metrics”. In: Proceedings Fourteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum.Google Scholar
Slade, S, 1991. “Case-based reasoning: A research paradigm”. Al Magazine 42–55.Google Scholar
Smith, EE, Adams, N and Schorr, D, 1978. “Fact retrieval and the paradox of interference”. Cognitive Psychology 10 438–464.CrossRefGoogle Scholar
Steels, L, 1990. “Components of expertise”. AI Magazine Summer, 28–50.Google Scholar
Sycara, EP, 1987. Resolving adversial conflicts: An approach to Integrating Case-Based and Analytic Methods. Technical Report GIT-ICS-87/26, Georgia Institute of Technology, School of Information and Computer Science.Google Scholar
Sycara, K, 1992. “CADET: a case-based synthesis tool for engineering design”. International Journal of Expert Systems 4(2) 157–188.Google Scholar
Sycara, K and Navichandra, D, 1992. “Retrieval strategies in a case-based design system”. In: Tong, C and Sriram, D (eds.), Artificial Intelligence in Engineering Design Vol. 2. Academic Press.Google Scholar
Tulving, E, 1977. “Episodic and semantic memory”. In: Tulving, E and Donaldson, W (eds.), Organisation of Memory, pp 381–403. Academic Press.Google Scholar
Vargas, JE and Raj, S, 1993. “Developing maintainable expert systems using case-based reasoning”. Expert Systems 10(4) 219–225.CrossRefGoogle Scholar
Venkatamaran, S, Krishnan, R and Rao, KK, 1993. “A rule-case based system for image analysis”. In: Proc. 1st. European Workshop on Case-Based Reasoning, Posters & Presentations 2 410–415.Google Scholar
Watson, ID, Basden, A and Brandon, PS, 1992a. “The client centred approach: expert system development”. Expert Systems 9(4) 181–188.CrossRefGoogle Scholar
Watson, ID, Basden, A and Brandon, PS, 1992b. “The client centred approach: expert system maintenance”. Expert Systems 9(4) 189–196.CrossRefGoogle Scholar
Watson, ID and Abdullah, S, 1994. “Developing case-based reasoning systems: a case study in diagnosing building defects”. In: Proc. lEE Colloquium on Case-Based Reasoning: Prospects for Applications. Digest No: 1994/057, pp 1/1–1/3.Google Scholar
Wielinga, BJ, Schreiber, ATh and Breuker, JA, 1992. “KADS: A modelling approach to knowledge engineering”.Knowledge Acquisition 4(1).CrossRefGoogle Scholar
Wittgenstein, L, 1953. Philosophical Investigations. Blackwell.Google Scholar
Yang, S and Robertson, D, 1994. “A case-based reasoning system for regulatory information”. In: Proc. lEE Colloquium on Case-Based Reasoning: Prospects for Applications. Digest No: 1994/057, pp 3/1–3/3.Google Scholar