Overview

Case-based reasoning (CBR) is a paradigm for reasoning and learning in artificial intelligence, with research efforts and 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 should make the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a companion for a CBR or introductory AI textbook.

Full Product Details

Author:   David Leake (Indiana University)
Publisher:   MIT Press Ltd
Imprint:   MIT Press
Dimensions:   Width: 15.20cm , Height: 3.00cm , Length: 22.90cm
Weight:   0.680kg
ISBN:  

9780262621106

ISBN 10:   026262110
Pages:   525
Publication Date:   13 August 1996
Recommended Age:   From 18
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Unknown
Availability:   Out of stock  

Table of Contents

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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