Techniques for Enterprise Systems To order this title, and for more information, click here
By Ian Watson, University of Auckland
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
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