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Publications » Computers » Expert Systems

Applying Case-Based Reasoning

Price £42.99

temporarily out of stock

Applying Case-Based Reasoning

Ian Watson

ISBN 1558604626
Pages 290

Description
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.

Contents
Preface 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 Implementation 4.1.5 Impact 4.2 Kaye Presteigne—Wayland 4.2.1 The Pressure Die Design Problem 4.2.2 Implementation 4.2.3 Case Retrieval 4.2.4 Case Adaptation 4.2.5 Benefits of Wayland 4.2.6 Summary of Benefits 4.3 British Airways—CASELine 4.3.1 The Problem 4.3.2 Implementation 4.3.3 Impact 4.4 Deloitte & Touche—Top Management Fraud 4.4.1 Implementation 4.4.2 Evaluation 4.4.3 User Evaluation 4.4.4 Summary 4.5 Summary 4.6 Further Reading Chapter 5: CBR and Customer Service 5.1 Help-Desk Architecture 5.2 The Compaq SMART System 5.2.2 The Solution 5.2.3 SMART Implementation 5.2.4 Impact 5.3 Broderbund—The GizmoTapper 5.3.1 The Problem 5.3.2 The Solution 5.3.3 Implementation 5.3.4 Impact 5.4 Legal & General—SWIFT 5.4.1 The Problem 5.4.2 The Solution 5.4.3 Implementation 5.4.4 Impact 5.5 Summary 5.6 Further Reading Chapter 6: CBR Software Tools 6.1 ART Enterprise 6.2 Case-1 6.3 CaseAdvisor 6.4 CasePower 6.5 CBR3 (CBR Express, CasePoint, Generator, Tester, and CasePoint WebServer) 6.6 Eclipse—The Easy Reasoner 6.7 ESTEEM 6.8 KATE 6.9 ReCall 6.10 ReMind 6.11 Other CBR Tools 6.11.1 CASUEL 6.11.2 CASPIAN 6.11.3 Recon 6.11.4 CBRWorks 6.11.5 Public Domain CBR Software 6.12 Summary 6.13 Vendor Information 6.14 Further Reading Chapter 7: Building a Diagnostic Case-Base 7.1 The Problem 7.1.1 The Existing Records 7.1.2 Analyzing the Cases 7.1.3 Authoring the First Case 7.1.4 Scoring Questions 7.1.5 Creating Actions 7.1.6 Adding More Cases 7.2 Preparing the Case-Base for Use 7.4 Acquiring New Experiences as Cases 7.5 Using Rules 7.6 Summary 7.7 Further Reading Chapter 8: Building, Testing, and Maintaining Case-Bases 8.1 Characterizing a Good Case-Base 8.1.1 Representative Cases 8.1.2 Case Distribution 8.2 Tool Support 8.3 Testing Case-Bases 8.3.1 Check Retrieval Accuracy 8.3.2 Check Retrieval Consistency 8.3.3 Check for Case Duplication 8.3.4 Check Case Coverage 8.3.5 Global System Verification Tests 8.4 Maintaining Case-Bases 8.4.1 Obtain Case Utilization Statistics 8.4.2 Repeat the Verification Tests 8.5 Summary 8.6 Further Reading Chapter 9: Conclusion 9.1 Learning Review 9.2 Assumptions and Key Concepts 9.2.1 CBR Assumptions 9.2.2 Similarity Is the Key 9.2.3 Case-Bases Must Be Large to be Useful 9.2.4 CBR Is Just Hype 9.3 The Case for Case-Based Reasoning 9.3.1 Corporate Memory 9.3.2 Case-Based Expert Systems 9.3.3 Case-Based Information Retrieval 9.3.4 CBR Is a Complementary Technique 9.4 The Way Ahead 9.5 Further Reading Chapter 10: Bibliography 10.1 Essential Readings 10.2 The Origins of CBR 10.2.1 History 10.2.2 Feasibility and Uses of CBR 10.2.3 Background and Review of CBR 10.3 CBR Techniques 10.3.1 Representation 10.3.2 Indexing 10.3.3 Memory Organization 10.3.4 Retrieval 10.3.5 Adaptation and Repair 10.4 Applied CBR 10.4.1 CBR Software Tools 10.4.2 CBR Demonstrators 10.4.3 Commercial Applications 10.5 Hybrid CBR 10.5.1 CBR and Analogy 10.6 Internet Information Sources 10.6.1 AI-CBR 10.6.2 CBR-MED 10.6.3 The European CBR Newsletter 10.6.4 Other Internet Sites Series: The Morgan Kaufmann Series in Artificial Intelligence