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

AuthorsLópez de Mántaras, Ramón ; McSherry, David; Bridge, Derek; Leake, David; Smyth, Barry; Craw, Susan; Faltings, Boi; Maher, Mary L.; Cox, Michael T.; Forbus, Kenneth; Keane, Mark; Aamodt, Agnar; Watson, Ian
KeywordsArtificial Intelligence
Case-Based Reasoning
Issue Date2006
PublisherCambridge University Press
CitationThe Knowledge Engineering Review, 2006, 20 (3), 215-240.
AbstractCase-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.
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