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

  • Ramon Lopez De Mantaras
  • , David Mcsherry
  • , Derek Bridge
  • , David Leake
  • , Barry Smyth
  • , Susan Craw
  • , Boi Faltings
  • , Mary Lou Maher
  • , Michael T. Cox
  • , Kenneth Forbus
  • , Mark Keane
  • , Agnar Aamodt
  • , Ian Watsoni
  • CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
  • Ulster University
  • Indiana University Bloomington
  • University College Dublin
  • Robert Gordon University
  • Swiss Federal Institute of Technology Lausanne
  • University of Sydney
  • BBN Technologies
  • Northwestern University
  • Norwegian University of Science and Technology
  • The University of Auckland

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)215-240
Number of pages26
JournalKnowledge Engineering Review
Volume20
Issue number3
DOIs
Publication statusPublished - Sep 2005

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