Knowledge Acquisition for Expert Systems -

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Résumé :
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.

Sommaire:
1 Knowledge Acquisition-An Introductory Framework.- 1. Introduction.- 2. The Knowledge Acquisition Process: Mining Is a Misguided Analogy.- 3. Knowledge Domains: Capturing or Creating a Language.- 4. Tasks: Defining the Problem That the Expert System is?Designed to Solve.- 5. System Modality: Defining the Set of Tasks.- 6. Users: Acquiring Their Knowledge.- 7. An Alternative Approach: Identifying Weaknesses in Human?Reasoning.- 8. Concluding Guidelines for Knowledge Engineers.- 9. Brief Overview of Chapters.- 10. References.- 2 Use of Models in the Interpretation of Verbal Data.- 1. Problems and Solutions in Building Expert Systems.- 2. A Methodology for Knowledge Acquisition.- 3. Interpretation of Data on Expertise.- 4. Concluding Remarks.- 5. Guidelines Summary.- 6. References.- 3 Knowledge Acquisition by Analysis of Verbatim Protocols.- 1. Introduction.- 2. Design of the Experiment.- 3. The Nephrotic Syndrome.- 4. Analysis of the Transcript.- 5. The Domain Model-Structural Description.- 6. Qualitative Simulation in the Explanation.- 7. The Domain Model-Qualitative Description of State.- 8. The Domain Model-Qualitative Simulation.- 9. Conclusion.- 10. Postscript.- 11. References.- 4 A Systematic Study of Knowledge Base Refinement in the Diagnosis of Leukemia.- 1. Introduction.- 2. Leukemia Diagnosis.- 3. Knowledge Elicitation and Expert System Development.- 4. System Performance.- 5. Analysis of Disagreements between System and Expert.- 6. Discussion.- 7. Guidelines Summary.- 8. References.- 5 Knowledge Elicitation Involving Teachback Interviewing.- 1. The Knowledge Elicitation Process.- 2. Case Studies.- 3. Mediating Representation-SGN.- 4. Discussion.- 5. Guidelines Summary.- 6. References.- 6 An Interactive Knowledge-Elicitation Technique using PersonalConstruct Technology.- 1. Knowledge Engineering.- 2. Personal Construct Psychology.- 3. What Is a Repertory Grid?.- 4. Techniques for Repertory Grid Elicitation and Analysis.- 5. Soft Systems Analysis.- 6. PLANET: A Computer-Based Knowledge-Engineering System.- 7. PEGASUS in Action.- 8. ENTAIL in Action.- 9. Validation.- 10. Guidelines Summary.- 11. References.- 7 Different Techniques and Different Aspects on Declarative Knowledge.- 1. Introduction.- 2. Methods.- 3. Using the Knowledge Base.- 4. Future Research.- 5. Guidelines Summary.- 6. References.- 8 Role of Induction in Knowledge Elicitation.- 1. Introduction.- 2. Induction.- 3. A Case Study.- 4. Conclusion.- 5. References.