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Editorial Reviews
Product Description
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.
Book Info
Presents a selection of recent progress, issues, and directions for the future of case-based reasoning. Also, presents lessons learned about how to design CBR systems and how to apply them to real-world problems. Paper.
See all Editorial Reviews
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.
Book Info
Presents a selection of recent progress, issues, and directions for the future of case-based reasoning. Also, presents lessons learned about how to design CBR systems and how to apply them to real-world problems. Paper.
See all Editorial Reviews
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- Case-Based Reasoning Research and Development: Third International Conference on Case-Based Reasoning, ICCBR-99, Seeon Monastery, Germany, July 27-30, ... (Lecture Notes in Computer Science) by Klaus-Dieter Althoff on page 217, page 231, and page 495
- Practical Aspects of Knowledge Management by Dimitris Karagiannis on page 143, and page 645
- Managing Knowledge with Artificial Intelligence: An Introduction with Guidelines for Nonspecialists by Kevin C. Desouza in Back Matter (1), and Back Matter (2)
- Applying Case-Based Reasoning: Techniques for Enterprise Systems (The Morgan Kaufmann Series in Artificial Intelligence) (The Morgan Kaufmann Series in Artificial Intelligence) by Ian Watson in Back Matter (1), and Back Matter (2)
- Designing Smart Homes: The Role of Artificial Intelligence (Lecture Notes in Computer Science) by Juan Carlos Augusto on page 128, and page 130
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