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Outline

Title
Abstract
Introduction
Characterisation of CBR Methods
Retrieving a Best Matching Case
Reusing a Past Case
Retaining a Case for Learning
Conclusions and Future Trends
References

Case-Based Reasoning-An Introduction

Profile image of Agnar AamodtAgnar Aamodt

2000

Abstract

Case-based reasoning is a recent approach to knowledge-based problem solving and decision support: A new problem is solved by remembering a previous similar situation and by reusing information and knowledge of that situation. Let us illustrate this by looking at some typical problem solving situations: A physician is examining a patient in his office. He gets a reminding to a patient that he treated two weeks ago. Assuming that the reminding was caused by a similarity of important symptoms, the physician uses the diagnosis and treatment of the previous patient to determine the disease and treatment for the patient in front of him. A financial consultant working on a difficult credit decision task uses a reminding to a previous case, which involved a company in similar trouble as the current one, to recommend that the loan application should be refused. A drilling engineer has experienced several dramatic blow out situations. He is quickly reminded of one of these situations when th...

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References (37)

  1. Aamodt, A., (1989) Towards robust expert systems that learn from experience -an architectural framework. In John Boose, Brian Gaines, Jean-Gabriel Ganascia (eds.): EKAW-89; Third European Knowledge Acquisition for Knowledge-Based Systems Workshop , Paris, July 1989. pp 311-326.
  2. Aamodt, A. (1991). A knowledge-intensive approach to problem solving and sustained learning, Ph.D. dissertation, University of Trondheim, Norwegian Institute of Technology, May 1991. (University Microfilms PUB 92-08460)
  3. Aamodt, A., Explanation-driven case-based reasoning, in: S. Wess, K. Althoff, M. Richter (eds.):Topics in Case-based reasoning. Springer Verlag, 1994, pp 274-288.
  4. Aamodt, A. and Plaza, E. Case-Based Reasoning: Foundational issues, methodological variations, and system approaches, AI Communications, Vol.7, No.1, March (1994) 39-59.
  5. Allen, B.P, Case-based reasoning: Business applications, Communication of the ACM 37 (3), 1994, 40-44.
  6. Althoff, K.D (1989). Knowledge acquisition in the domain of CNC machine centers; the MOLTKE approach. In John Boose, Brian Gaines, Jean-Gabriel Ganascia (eds.): EKAW-89; Third European Workshop on Knowledge-Based Systems, Paris, July 1989. pp 180-195.
  7. Bareiss, R. (1989). Exemplar-based knowledge acquisition: A unified approach to concept representation, classification, and learning. Boston, Academic Press.
  8. Brown, B. and Lewis, L. (1991): A case-based reasoning solution to the problem of redundant resolutions of non-conformances in large scale manufacturing. In: R. Smith, C. Scott (eds.): Innovative Applications for Artificial Intelligence 3. MIT Press.
  9. CBR-91,Proceedings from the Case-Based Reasoning Workshop, Washington D.C., May 8-10, 1991. Sponsored by DARPA. Morgan Kaufmann.
  10. EWCBR-93,First European Workshop on Case-based Reasoning, Posters and Presentations, 1-5 November 1993. Vol. I-II. University of Kaiserslautern.
  11. The FABEL Consortium (1993): Survey of FABEL. FABEL Report No. 2, GMD, Sankt Augustin. Hammond, K.J (1989): Case-based planning. Academic Press.
  12. Harmon, P. (1992): Case-based reasoning III, Intelligent Software Strategies, VIII (1).
  13. Hennessy, D. and Hinkle, D. (1992). Applying case-based reasoning to autoclave loading. IEEE Expert 7(5), pp. 21-26.
  14. Hinrichs, T.R. (1992): Problem solving in open worlds. Lawrence Erlbaum Associates. IEEE Expert (1992): 7(5), Special issue on case-based reasoning. October 1992
  15. Keane, M. (1988): Where's the Beef? The Absence of Pragmatic Factors in Pragmatic Theories of Analogy In: Proc. ECAI-88, pp. 327-332
  16. Kedar-Cabelli, S. (1988): Analogy -from a unified perspective. In: D.H. Helman (ed.), Analogical reasoning. Kluwer Academic, 1988. pp 65-103.
  17. Kitano, H. (1993): Challenges for massive parallelism. IJCAI-93, Proceedings of the Thirteenth International Conference on Artificial Intelligence, Chambery, France, 1993. Morgan Kaufman 1993. pp. 813-834.
  18. Kolodner, J. (1983a): Maintaining organization in a dynamic long-term memory. Cognitive Science, Vol.7, s.243-280.
  19. Koton, P. (1989): Using experience in learning and problem solving. Massachusetts Institute of Technology, Laboratory of Computer Science (Ph.D. diss, October 1988). MIT/LCS/TR-441. 1989.
  20. López, B. and Plaza, E. (1990): Case-based learning of strategic knowledge. Centre d'Estudis Avançats de Blanes, CSIC, Report de Recerca GRIAL 90/14. Blanes, Spain, October 1990.
  21. Manago, M., Althoff, K-D. and Traphöner, R. (1993): Induction and reasoning from cases. In: ECML - European Conference on Machine Learning, Workshop on Intelligent Learning Architectures. Vienna, April 1993.
  22. Plaza, E. and López de Mántaras, R (1990): A case-based apprentice that learns from fuzzy examples. Proceedings, ISMIS, Knoxville, Tennessee, 1990. pp 420-427.
  23. Porter, B. and Bareiss, R. (1986): PROTOS: An experiment in knowledge acquisition for heuristic classification tasks. In: Proceedings of the First International Meeting on Advances in Learning (IMAL), Les Arcs, France, pp. 159-174.
  24. Richter, A.M. and Weiss, S. (1991): Similarity, uncertainty and case-based reasoning in PATDEX. In R.S. Boyer (ed.): Automated reasoning, essays in honour of Woody Bledsoe. Kluwer, pp. 249-265.
  25. Rissland, E. (1983): Examples in legal reasoning: Legal hypotheticals. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, IJCAI, Karlsruhe.
  26. Ross, B.H (1989): Some psychological results on case-based reasoning. Case-Based Reasoning Workshop , DARPA 1989. Pensacola Beach. Morgan Kaufmann. pp. 144-147).
  27. Schank, R. (1982): Dynamic memory; a theory of reminding and learning in computers and people. Cambridge University Press.
  28. Schank, R. and Leake, D. (1989): Creativity and learning in a case-based explainer. Artificial Intelligence, Vol. 40, no 1-3. pp 353-385.
  29. Schult, T. (1992): Werkzeuge für fallbaseierte systeme. Künstliche Intelligenz 3(92).
  30. Sharma, S., Sleeman, D (1988): REFINER; a case-based differential diagnosis aide for knowledge acquisition and knowledge refinement. In: EWSL 88; Proceedings of the Third European Working Session on Learning, Pitman. pp 201-210.
  31. Simoudis, E. (1992): Using case-based reasoning for customer techical support. IEEE Expert 7(5), pp. 7-13.
  32. Simpson, R.L. (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.
  33. Stanfill, C and Waltz, D. (1988): The memory based reasoning paradigm. In: Case based reasoning. Proceedings from a workshop, Clearwater Beach, Florida, May 1988. Morgan Kaufmann Publ. pp.414- 424.
  34. Strube, G. and Janetzko, D. (1990): Episodishes Wissen und Fallbasierte Schliessen: Aufgabe fur die Wissendsdiagnostik und die Wissenspsychologie. Schweizerische Zeitschrift fur Psychologie, 49, 211- 221.
  35. Sycara, K. (1988): Using case-based reasoning for plan adaptation and repair. Proceedings Case-Based Reasoning Workshop, DARPA. Clearwater Beach, Florida. Morgan Kaufmann, pp. 425-434.
  36. Veloso, M.M. and Carbonell, J. (1993): Derivational analogy in PRODIGY. In Machine Learning 10(3), pp. 249-278.
  37. Venkatamaran, S., Krishnan, R. and Rao, K.K. (1993): A rule-rule-case based system for image analysis. In: First European Workshop on Case-based Reasoning, Posters and Presentations, 1-5 November 1993. Vol. II. University of Kaiserslautern, pp. 410-415.

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Case-based reasoning means using old experiences to understand and solve new problems. In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem. Casebased reasoning can mean adapting old solutions to meet new demands; using old cases to explain new situations; using old cases to critique new solutions; or reasoning from precedents to interpret a new situation (much like lawyers do) or create an equitable solution to a new problem (much like labor mediators do). This paper discusses the processes involved in case-based reasoning and the tasks for which case-based reasoning is useful.

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Study of Case-Based Reasoning System
IJSRD - International Journal for Scientific Research and Development

Case-Based Reasoning is a problem solving paradigm that in many respects is fundamentally different from other major AI approaches. Instead of relying solely on general knowledge of a problem domain, or making associations along generalized relationships between problem descriptors and conclusions, CBR is able to utilize the specific knowledge of previously experienced, concrete problem situations (cases). A new problem is solved by finding a similar past case, and reusing it in the new problem situation. A second important difference is that CBR also is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved, making it immediately available for future problems. The CBR field has grown rapidly over the last few years, as seen by its increased share of papers at major conferences, available commercial tools, and successful applications in daily use. A CBR tool should support the four main processes of CBR: retrieval, reuse, revision and retention.

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Case-based reasoning: Foundational issues, methodological variations, and system approaches
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Ai Communications, 1994

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

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