Case-Based Reasoning: Experiences, Lessons, and Future Directions

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. This Book Presents A Selection Of Recent Progress, Issues, And Directions For The Future Of Case-based Reasoning. It Includes Chapters Addressing Fundamental Issues And Approaches In Indexing And Retrieval, Situation Assessment And Similarity Assessment, And In Case Adaptation. Those Chapters Provide A Case-based View Of Key Problems And Solutions In Context Of The Tasks For Which They Were Developed. It Also Presents Lessons Learned About How To Design Cbr Systems And How To Apply Them To Real-world Problems. The Final Chapters Include A Perspective On The State Of The Field And The Most Important Directions For Future Impact. The Case Studies Presented Involve A Broad Sampling Of Tasks, Such As Design, Education, Legal Reasoning, Planning, Decision Support, Problem-solving, And Knowledge Navigation. In Addition, They Experimentally Examine One Of The Fundamental Tenets Of Cbr, That Reasoning From Prior Experiences Improves Performance. The Chapters Also Address Other Issues That, While Not Restricted To Cbr Per Se, Have Been Vigorously Attacked By The Cbr Community, Including Creative Problem-solving, Strategic Memory Search, And Opportunistic Retrieval. This Volume Provides A Vision Of The Present, And A Challenge For The Future, Of Case-based Reasoning Research And Applications.--book Jacket. 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, David B. Skalak, And M. Timur Friedman -- A Case-based Approach To Knowledge Navigation / Kristian J. Hammond, Robin Burke And Kathryn Schmitt -- Flexible Strategy Learning Using Analogical Replay Of Problem Solving Episodes / Manuela M. Veloso -- Design à La Déjà Vu: Reducing The Adaptation Overhead / Barry Smyth And Mark T. Keane -- Multi-plan Retrieval And Adaptation In An Experience-based Agent / Ashwin Ram And Anthony G. Francis, Jr. -- Learning To Improve Case Adaptation By Introspective Reasoning And Cbr / David B. Leake, Andrew Kinley, And David Wilson -- Systematic Evaluation Of Design Decisions In Case-based Reasoning Systems / Juan Carlos Santamaría 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, Evangelos Simoudis, And David Hinkle -- 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. Edited By David B. Leake. Includes Bibliographical References (p. [389]-411) And Index. 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.