Automating Knowledge Acquisition for Expert Systems

1. Introduction.- 2. MORE: From Observing Knowledge Engineers to Automating Knowledge Acquisition.- 2.1. Introduction.- 2.1.1. Goals for MORE.- 2.1.2. MORE's Problem-Solving Strategy.- 2.1.3. An Overview.- 2.2. Strategies for Knowledge Acquisition.- 2.2.1. The Genesis of the Strategies.- 2.2.2. Summary.- 2.3. Knowledge Representation.- 2.3.1. The Event Model.- 2.3.2. An Example Representation.- 2.4. From Event Model to Rules.- 2.4.1. Generating Rules.- 2.4.2. Advice about Confidence Factors.- 2.5. Strategy Evocation and Implementation - Advice for Improving the Knowledge Base.- 2.6. The Problem-Solver Revisited.- 2.7. Learning from MORE.- 2.7.1. MORE's Problems.- 2.7.2. Improving on MORE with TDE.- 2.7.3. User Interface.- 2.8. Conclusion.- 3. MOLE: A Knowledge-Acquisition Tool for Cover-and-Differentiate Systems.- 3.1. Introduction.- 3.2. MOLE's Problem-Solving Method and Knowledge Roles.- 3.2.1. The Cover-and-Differentiate Problem-Solving Method.- 3.2.2. Diagnosing Inefficiencies in a Power Plant.- 3.3. Acquiring the Knowledge Base.- 3.3.1. Acquiring the Initial Symptoms.- 3.3.2. Acquiring Covering Knowledge.- 3.3.3. Acquiring Differentiating Knowledge.- 3.4. Handling Uncertainty.- 3.5. Identifying Weaknesses in the Knowledge Base.- 3.5.1. Refining Covering Knowledge.- 3.5.2. Refining Differentiating Knowledge.- 3.6. MOLE's Scope.- 3.7. Conclusion.- 4. SALT: A Knowledge-Acquisition Tool for Propose-and-Revise Systems.- 4.1. Introduction.- 4.2. Acquiring Relevant Knowledge Pieces.- 4.3. Analyzing How the Pieces Fit Together.- 4.3.1. General Completeness.- 4.3.2. Compilability.- 4.3.3. Convergence.- 4.4. Compiling the Knowledge Base.- 4.5. Explaining Problem-Solving Decisions.- 4.6. Evaluating Test Case Coverage.- 4.7. Understanding SALT's Scope.- 4.7.1. Acquiring Relevant Knowledge Pieces.- 4.7.2. Compiling the Knowledge Base.- 4.8. Conclusion.- 5. KNACK: Sample-Driven Knowledge Acquisition for Reporting Systems.- 5.1. Introduction.- 5.2. The Presupposed Problem-Solving Method and Its Knowledge Roles.- 5.3. Acquiring Knowledge.- 5.3.1. Acquiring the Sample Report.- 5.3.2. Acquiring the Domain Model.- 5.3.3. Generalizing the Sample Report.- 5.3.4. Demonstrating Understanding of the Sample Report.- 5.3.5. Defining, Generalizing, and Correcting Strategies.- 5.4. Analyzing the Knowledge Base.- 5.5. Rule Generation.- 5.6. Combining Problem-Solving Methods.- 5.6.1. The Combined Method.- 5.6.2. Acquiring Additional Knowledge.- 5.6.3. Generating Additional Rules.- 5.7. KNACK's Scope.- 5.7.1. KNACK Tasks.- 5.7.2. Some Performance Data.- 5.8. Conclusion.- 6. SIZZLE: A Knowledge-Acquisition Tool Specialized for the Sizing Task.- 6.1. Introduction.- 6.2. Problem-Solving Strategies for Sizing.- 6.2.1. Sizing Knowledge.- 6.2.2. Methods for Computer Sizing and Knowledge Acquisition.- 6.2.3. The Choice: Extrapolation from a Similar Case.- 6.3. Using SIZZLE.- 6.4. Knowledge Representation and Proceduralization.- 6.5. The Scope of the Knowledge-Acquisition Tool.- 6.6. Conclusion.- 7. RIME: Preliminary Work Toward a Knowledge-Acquisition Tool.- 7.1. Introduction.- 7.2. A Knowledge-Acquisition Tool for XCON?.- 7.2.1. Acquiring Knowledge.- 7.2.2. Generating an Application.- 7.2.3. Issues addressed by RIME.- 7.3. What is RIME?.- 7.3.1. Control.- 7.3.2. Focus of Attention.- 7.3.3. Organizational Structures.- 7.3.4. Programming Conventions.- 7.4. Scope of Applicability.- 7.5. Future Directions.- 8. Preliminary Steps Toward a Taxonomy of Problem-Solving Methods.- 8.1. Introduction.- 8.2. A Few Data Points.- 8.2.1. MOLE.- 8.2.2. YAKA.- 8.2.3. SALT.- 8.2.4. KNACK.- 8.2.5. SIZZLE.- 8.2.6. SEAR.- 8.3. Conclusions.- References.