Retrieval, reuse, revision and retention in case-based reasoning

By R. L. D. Mántaras, David McSherry, Derek G. Bridge, David B. Leake, Barry Smyth, Susan Craw, Boi Faltings, Mary Lou Maher, Michael T. Cox, Kenneth D. Forbus and 3 more. 2005

636 citations recorded by the source corpus.

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Agnar Aamodt
2. Jörg Cassens

Citations in the corpus

• Explanatory Capabilities in the CREEK Knowledge-Intensive Case-Based Reasoner (2008, background): “Such an approach tends to be more computational expensive than purely syntactical comparison, yet it often produces more relevant cases [12].”