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

Abstract

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.