Chapter 1: What Is Case-Based Reasoning? 1.1 Representing Information 1.2 Representing Data in a Computer 1.3 Object Databases 1.4 A More Powerful Representation 1.5 Rule-Based Expert Systems 1.6 The Limitations of Rules 1.7 Elephants Never Forget! 1.8 I've Got the Answer, What's the Question? 1.9 Summary 1.10 Further Reading
Chapter 2: Understanding CBR 2.1 The CBR-Cycle 2.2 A Short History of Case-Based Reasoning 2.3 Case-Based Reasoning Techniques 2.3.1 Case Representation 2.3.2 Indexing 2.3.3 Storage 2.3.4 Retrieval 2.3.5 Adaptation 2.4 Summary 2.5 Further Reading
Chapter 3: The Application of CBR 3.1 A Classification of Applications 3.1.1 Classification Tasks 3.1.2 Synthesis Tasks 3.2 CBR vs. Other Techniques 3.2.1 CBR and Information Retrieval 3.2.2 CBR vs. Statistical Techniques 3.2.3 CBR vs. Rule-Based Expert Systems 3.2.4 CBR vs. Machine Learning 3.2.5 CBR vs. Neural Networks 3.2.6 Summary of Technology Comparisons 3.3 Academic Demonstrators 3.3.1 Diagnosis 3.3.2 Planning 3.3.3 Legal Reasoning 3.3.4 Design 3.3.5 Analogous Reasoning 3.3.6 Arbitration 3.3.7 Adaptation 3.3.8 Tutoring 3.4 Summary 3.5 Further Reading
Chapter 4: Industrial Applications of CBR 4.1 Lockheed—CLAVIER 4.1.1 Case Representation 4.1.2 Case Retrieval 4.1.3 Case Adaptation 4.1.4 Imple
Case-based reasoning (CBR) is an intelligent-systems method that enables information managers to increase efficiency and reduce cost by substantially automating processes such as diagnosis, scheduling and design. A case-based reasoner works by matching new problems to "cases" from a historical database and then adapting successful solutions from the past to current situations. Organizations as diverse as IBM, VISA International, Volkswagen, British Airways, and NASA have already made use of CBR in applications such as customer support, quality assurance, aircraft maintenance, process planning, and decision support, and many more applications are easily imaginable.
It is relatively simple to add CBR components to existing information systems, as this book demonstrates. The author explains the principles of CBR by describing its origins and contrasting it with familiar information disciplines such as traditional data processing, logic programming, rule-based expert systems, and object-oriented programming. Through case studies and step-by-step examples, he goes on to show how to design and implement a reliable, robust CBR system in a real-world environment. Additional resources are provided in a survey of commercially available CBR tools, a comprehensive bibliography, and a listing of companies providing CBR software and services.
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- © Morgan Kaufmann 1997
- 1st July 1997
- Morgan Kaufmann
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Ian Watson is Senior Lecturer in Intelligent Systems at the University of Salford in the United Kingdom, and one of the world's most active researchers in CBR. He became interested in CBR after realizing its promise for industrial applications and its comparative ease of implementation. He is a member of the Research Centre for the Built and Human Environment (BUHU), where he continues his investigations of intelligent-systems methodologies and object-oriented programming and maintains AI-CBR, the center for CBR information on the World Wide Web.
University of Auckland