CASE-BASED REASONING: EXPERIENCES, LESSONS, AND FUTURE DIRECTIONS

David B. Leake, Indiana University, Editor

Preface:

Case-based reasoning (CBR) is now a mature subfield of artificial intelligence. The fundamental principles of case-based reasoning have been established, and numerous applications have demonstrated its role as a useful technology. Recent progress has also revealed new opportunities and challenges for the field. This book presents experiences in CBR that illustrate the state of the art, the lessons learned from those experiences, and directions for the future.

True to the spirit of CBR, this book examines the field in a primarily case-based way. Its chapters provide 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. These issue-oriented case studies of experiences with particular projects provide a view of the principles of CBR, what CBR can do, how to attack problems with case-based reasoning, and how new challenges are being addressed. The case studies are supplemented with commentaries from leaders in the field providing individual perspectives on the state of CBR and its future impact.

This book provides experienced CBR practitioners with a reference to recent progress in case-based reasoning research and applications. It also provides an introduction to CBR methods and the state of the art for students, AI researchers in other areas, and developers starting to build case-based reasoning systems. It presents experts and non-experts alike with visions of the most promising directions for new progress and for the roles of the next generation of CBR systems.

Contents:

1. CBR in Context: The Present and Future
   David B. Leake.
2. A Tutorial Introduction to Case-Based Reasoning
   Janet L. Kolodner and David B. Leake
3. Indexing Evaluations of Buildings to Aid Conceptual Design
   Anna L. Griffith and Eric A. Domeshek
4. Towards More Creative Case-Based Design Systems
   Linda M. Wills and Janet L. Kolodner
5. Retrieving Stories for Case-Based Teaching
   Robin Burke and Alex Kass
6. Using Heuristic Search to Retrieve Cases that Support Arguments
   Edwina L. Rissland, David B. Skalak, and M. Timur Friedman
7. A Case-Based Approach to Knowledge Navigation
   Kristian J. Hammond, Robin Burke and Kathryn Schmitt
8. Flexible Strategy Learning Using Analogical Replay of Problem Solving Episodes
   Manuela M. Veloso
9. Design a la Deja Vu: Reducing the Adaptation Overhead
   Barry Smyth and Mark T. Keane
10. Multi-plan Retrieval and Adaptation in an Experience-Based Agent
    Ashwin Ram and Anthony G.Francis, Jr.
11. Learning to Improve Case Adaptation by Introspective Reasoning and CBR
    David B. Leake, Andrew Kinley, and David Wilson
12. Systematic Evaluation of Design Decisions in Case-Based Reasoning Systems
    Juan Carlos Santamaria and Ashwin Ram
13. The Experience Sharing Architecture: A Case Study in Corporate- Wide Case-Based Software Quality Control
    Hiroaki Kitano and Hideo Shimazu
14. Case-Based Reasoning: Expectations and Results
    William Mark, Evangelos Simoudis, and David Hinkle
15. Goal-Based Scenarios: Case-Based Reasoning Meets Learning by Doing
    Roger C. Schank
16. Making the Implicit Explicit: Clarifying the Principles of Case-Based Reasoning
    Janet L. Kolodner
17. What next? The Future of CBR in Postmodern AI
    Christopher K. Riesbeck

Other home pages for the book

• AAAI Press page
• MIT Press page

Erratum