Knowledge Acquisition for Expert Systems

1. Front Matter

   Pages i-xiii

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2. Knowledge Acquisition

   • Alison L. Kidd

   Pages 1-16

3. Use of Models in the Interpretation of Verbal Data

   • Joost Breuker, Bob Wielinga

   Pages 17-44

4. Knowledge Acquisition by Analysis of Verbatim Protocols

   • Benjamin Kuipers, Jerome P. Kassirer

   Pages 45-71

5. A Systematic Study of Knowledge Base Refinement in the Diagnosis of Leukemia

   • John Fox, Christopher D. Myers, Melvyn F. Greaves, Susan Pegram

   Pages 73-90

6. Knowledge Elicitation Involving Teachback Interviewing

   • Leslie Johnson, Nancy E. Johnson

   Pages 91-108

7. An Interactive Knowledge-Elicitation Technique Using Personal Construct Technology

   • Mildred L. G. Shaw, Brian R. Gaines

   Pages 109-136

8. Different Techniques and Different Aspects on Declarative Knowledge

   • John G. Gammack

   Pages 137-163

9. Role of Induction in Knowledge Elicitation

   • Anna Hart

   Pages 165-189

10. Back Matter

    Pages 191-194

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Building an expert system involves eliciting, analyzing, and interpreting the knowledge that a human expert uses when solving problems. Expe­ rience has shown that this process of "knowledge acquisition" is both difficult and time consuming and is often a major bottleneck in the production of expert systems. Unfortunately, an adequate theoretical basis for knowledge acquisition has not yet been established. This re­ quires a classification of knowledge domains and problem-solving tasks and an improved understanding of the relationship between knowledge structures in human and machine. In the meantime, expert system builders need access to information about the techniques currently being employed and their effectiveness in different applications. The aim of this book, therefore, is to draw on the experience of AI scientists, cognitive psychologists, and knowledge engineers in discussing particular acquisition techniques and providing practical advice on their application. Each chapter provides a detailed description of a particular technique or methodology applied within a selected task domain. The relative strengths and weaknesses of the tech­ nique are summarized at the end of each chapter with some suggested guidelines for its use. We hope that this book will not only serve as a practical handbook for expert system builders, but also be of interest to AI and cognitive scientists who are seeking to develop a theory of knowledge acquisition for expert systems.

• Action
• algorithms
• computer
• performance
• simulation
• technology
• time

• Hewlett Packard Laboratories, Stoke Gifford, Bristol, England

  Alison L. Kidd

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