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Abstract

Case-based reasoning (CBR) is a problem solving methodology that focuses on reusing lessons obtained from previous (possibly generalized) experiences towards solving new problems (Kolodner, 1993; Aamodt & Plaza, 1994; Watson, 1999; Bergmann, 2002). Originally conceived by cognitive scientists, since 1993 the CBR community has focused primarily on issues of interest to artificial intelligence researchers and practitioners. Some research topics of particular interest include case representation and indexing, solution retrieval and adaptation, learning (e.g., case acquisition), and integrating case-based approaches with others. Some motivating applications have included those related to customer support, recommender systems, knowledge management, diagnosis, the health sciences, and legal reasoning.

References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  2. Aha, D.W., Breslow, L.A., Munoz-Avila, H.: Conversational case-based reasoning. Applied Intelligence 14(1), 9–32 (2001)zbMATHCrossRefGoogle Scholar
  3. Bergmann, R.: Experience management: Foundations, development methodology, and Internet-based applications. Springer, Berlin (2002)zbMATHGoogle Scholar
  4. Kolodner, J.: Case-based reasoning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  5. Watson, I.: CBR is a methodology not a technology. Knowledge Based Systems Journal 12(5-6), 303–308 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David W. Aha
    • 1
  1. 1.Head, Intelligent Decision Aids Group, Navy Center for Applied Research in Artificial IntelligenceNaval Research Laboratory (Code 5515)Washington, DCUSA

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