Case-based reasoning : experiences, lessons & future directions

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書誌事項

Case-based reasoning : experiences, lessons & future directions

edited by David B. Leake

AAAI Press , MIT Press, c1996

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注記

Includes bibliographical references (p. [389]-411) and index

内容説明・目次

内容説明

Case-based reasoning (CBR) is a flourishing paradigm for reasoning and learning in artificial intelligence, with major research efforts and burgeoning applications extending the frontiers of the field. This book provides an introduction for students as well as an up-to-date overview for experienced researchers and practitioners. It examines the field in a "case-based" way, through concrete examples of how key issues -- including indexing and retrieval, case adaptation, evaluation, and application of CBR methods -- are being addressed in the context of a range of tasks and domains. Complementing these case studies are commentaries by leading researchers on the lessons learned from experiences with CBR and visions for the roles in which case-based reasoning can have the greatest impact. A tutorial introduction by Janet Kolodner, one of the originators of CBR, and David Leake makes the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a suitable companion for a CBR or introductory AI textbook.

目次

  • CBR in context - the present and future, David B. Leake
  • a tutorial introduction to case-based reasoning, Janet L. Kolodner and David B. Leake
  • indexing evaluations of buildings to aid conceptual design, Anna L. Griffith and Eric A. Domeshek
  • towards more creative case-based design systems, Linda M. Wills and Janet L. Kolodner
  • retrieving stories for case-based teaching, Robin Burke and Alex Kass
  • using heuristic search to retrieve cases that support arguments, Edwina L. Rissland et al
  • a case-based approach to knowledge navigation, Kristian J. Hammond et al
  • flexible strategy learning using analogical replay of problem solving episodes, Manuela M. Veloso
  • design a la deja vu - reducing the adaptation overhead, Barry Smyth and Mark T. Keane
  • multi-plan retrieval and adaptation in an experience-based agent, Aswin Ram and Anthony G. Francis, Jr.
  • learning to improve case adaptation by introspective reasoning and CBR, David B. Leake et al
  • systematic evaluation of design decisions in case-based reasoning systems, Juan Carlos Santamaria and Ashwin Ram
  • the experience sharing architecture - a case study in corporate-wide case-based software quality control, Hiroaki Kitano and Hideo Shimazu
  • case-based reasoning - expectations and results, William Mark et al
  • goal-based scenarios - case-based reasoning meets learning by doing, Roger C. Schank
  • making the implicit explicit - clarifying the principles of case-based reasoning, Janet L. Kolodner
  • what next? the future of case-based reasoning in postmodern AI, Christopher K. Riesbeck.

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