Automating Knowledge Acquisition for Expert Systems

281 Pages·1988·9.01 MB·English

by  Sandra Marcus (auth.)

AUTOMATING KNOWLEDGE ACQUISITION FOR EXPERT SYSTEMS
THE KLUWER INTERNATIONAL SERIES
IN ENGINEERING AND COMPUTER SCIENCE
KNOWLEDGE REPRESENT AnON,
LEARNING AND EXPERT SYSTEMS
Consulting Editor
Tom Mitchell
Carnegie Mellon University
Other books in the series:
Universal Subgoaling and Chunking of Goal Hierarchies. J. Laird, P. Rosenbloom, A. Newell.
ISBN 0-89838-213-0.
Machine Learning: A Guide 10 Current Research. T. Mitchell, J. Carbonell, R. Michalski.
ISBN 0-C9838-214-9.
Machine Learning of InduClive Bias. P. Utgoff. ISBN 0-89838-223-8.
A Connectionist Machine for Genetic HillC/imbing. D. H. Ackley. ISBN 0-89838-236-X.
Learning From Good and Bad Data. P. D. Laird. ISBN 0-89838-263-7.
Machine Learning of Robot Assembly Plans. A. M. Segre. ISBN 0-89838-269-6.
AUTOMATING KNOWLEDGE
ACQUISITION FOR EXPERT SYSTEMS
edited by
Sandra Marcus
Boeing Computer Services
....
"
KLUWER ACADEMIC PUBLISHERS
Boston/Dordrecht/London
Distributors for North America:
Kluwer Academic Publishers
101 Philip Drive
Assinippi Park
Norwell, Massachusetts 02061, USA
Distributors for the UK and Ireland:
Kluwer Academic Publishers
Falcon House, Queen Square
Lancaster LAI IRN, UNITED KINGDOM
Distributors for all other countries:
Kluwer Academic Publishers Group
Distribution Centre
Post Office Box 322
3300 AH Dordrecht, THE NETHERLANDS
Library of Congress Cataloging-in-Publication Data
Automating knowledge acquisition for expert systems/edited by Sandra
Marcus.
p. cm.-(The Kluwer international series in engineering and
computer science. Knowledge representation, learning, and expert
systems)
Includes index.
ISBN-13: 978-1-4684-7124-3 e-ISBN-13: 978-1-4684-7122-9
DOl: 10.1007/978-1-4684-7122-9
1. Expert systems (Computer science) I. Marcus, Sandra.
II. Series.
QA 76. 76.E95A97 1988 88-21012
006.3 '3-dcI9 CIP
Copyright © 1988 by Kluwer Academic Publishers
Softcover reprint of the hardcover 1st edition 1988
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or
transmitted in any form or by any means, mechanical, photocopying, recording, or otherwise,
without the prior written permission of the publisher, Kluwer Academic Publishers, 101 Philip
Drive, Assinippi Park, Norwell, Massachusetts 02061.
Table of Contents
Contributing Authors xiii
Preface xv
1. Introduction, Sandra Marcus 1
2. MORE: From Observing Knowledge Engineers to 7
Automating Knowledge Acquisition, Gary Kahn
2.1. Introduction 7
2.1.1. Goals for MORE 7
2.1.2. MORE's Problem-Solving Strategy 8
2.1.3. An Overview 9
2.2. Strategies for Knowledge Acquisition 11
2.2.1. The Genesis of the Strategies 12
2.2.2. Summary 17
2.3. Knowledge Representation 17
2.3.1. The Event Model 17
2.3.2. An Example Representation 19
2.4. From Event Model to Rules 19
2.4.1. Generating Rules 20
2.4.2. Advice about Confidence Factors 23
2.5. Strategy Evocation and Implementation -- Advice for Improving 25
the Knowledge Base
2.6. The Problem-Solver Revisited 27
2.7. Learning from MORE 29
2.7.1. MORE's Problems 30
2.7.2. Improving on MORE with TDE 31
2.7.3. User Interface 33
2.8. Conclusion 33
3. MOLE: A Knowledge-Acquisition Tool for Cover-and- 37
Differentiate Systems, Larry Eshelman
3.1. Introduction 37
3.2. MOLE's Problem-Solving Method and Knowledge Roles 39
3.2.1. The Cover-and-Differentiate Problem-Solving Method 39
3.2.2. Diagnosing Inefficiencies in a Power Plant 44
3.3. Acquiring the Knowledge Base 47
3.3.1. Acquiring the Initial Symptoms 49
3.3.2. Acquiring Covering Knowledge 50
3.3.3. Acquiring Differentiating Knowledge 54
3.4. Handling Uncertainty 61
3.5. Identifying Weaknesses in the Knowledge Base 66
3.5.1. Refining Covering Knowledge 66
3.5.2. Refining Differentiating Knowledge 70
3.6. MOLE's Scope 76
vi Automating Knowledge Acquisition for Expert Systems
3.7. Conclusion 79
4. SALT: A Knowledge-Acquisition Tool for Propose-and- 81
Revise Systems, Sandra Marcus
4.1. Introduction 81
4.2. Acquiring Relevant Knowledge Pieces 83
4.3. Analyzing How the Pieces Fit Together 89
4.3.1. General Completeness 91
4.3.2. Compilability 91
4.3.3. Convergence 98
4.4. Compiling the Knowledge Base 104
4.5. Explaining Problem-Solving Decisions 112
4.6. Evaluating Test Case Coverage 117
4.7. Understanding SALT's Scope 118
4.7.1. Acquiring Relevant Knowledge Pieces 119
4.7.2. Compiling the Knowledge Base 120
4.8. Conclusion 121
5. KNACK: Sample-Driven Knowledge Acquisition for 125
Reporting Systems, Georg Klinker
5.1. Introduction 125
5.2. The Presupposed Problem-SOlving Method and Its Knowledge 130
Roles
5.3. Acquiring Knowledge 134
5.3.1. Acquiring the Sample Report 137
5.3.2. Acquiring the Domain Model 138
5.3.3. Generalizing the Sample Report 142
5.3.4. Demonstrating Understanding of the Sample Report 146
5.3.5. Defining, Generalizing, and Correcting Strategies 148
5.4. Analyzing the Knowledge Base 152
5.5. Rule Generation 156
5.6. Combining Problem-Solving Methods 160
5.6.1. The Combined Method 161
5.6.2. Acquiring Additional Knowledge 163
5.6.3. Generating Additional Rules 164
5.7. KNACK's Scope 165
5.7.1. KNACK Tasks 166
5.7.2. Some Performance Data 169
5.8. Conclusion 171
6. SIZZLE: A Knowledge-Acquisition Tool Specialized for 175
the Sizing Task, Daniel Offutt
6.1. Introduction 175
6.2. Problem-Solving Strategies for Sizing 180
6.2.1. Sizing Knowledge 181
vii
6.2.2. Methods for Computer Sizing and Knowledge Acquisition 182
6.2.3. The Choice: Extrapolation from a Similar Case 185
6.3. Using SIZZLE 186
6.4. Knowledge Representation and Proceduralization 193
6.5. The Scope of the Knowledge-Acquisition Tool 196
6.6. Conclusion 198
7. RIME: Preliminary Work Toward a Knowledge- 201
Acquisition Tool, Judith Bachant
7.1. Introduction 201
7.2. A Knowledge-Acquisition Tool for XCON? 202
7.2.1. Acquiring Knowledge 202
7.2.2. Generating an Application 204
7.2.3. Issues addressed by RIME 204
7.3. What is RIME? 204
7.3.1. Control 206
7.3.2. Focus of Attention 213
7.3.3. Organizational Structures 216
7.3.4. Programming Conventions 218
7.4. Scope of Applicability 222
7.5. Future Directions 223
8. Preliminary Steps Toward a Taxonomy of Problem- 225
Solving Methods, John McDermott
8.1. Introduction 225
8.2. A Few Data Points 234
8.2.1. MOLE 234
8.2.2. YA KA 238
8.2.3. SALT 242
8.2.4. KNACK 245
8.2.5. SIZZLE 250
8.2.6. SEAR 252
8.3. Conclusions 254
References 257
Index 267
List of Figures
Figure 2-1: Event Model of Drilling-Fluids Facts 20
Figure 2-2: Example of TDE's Multiwindow Display 34
Figure 3-1: Network Representation of a MOLE Knowledge Base 40
Figure 3-2: Diagnosing Engine Problems 44
Figure 3-3: The Initial Explanation Space for the Power Plant 54
Domain
Figure 3-4: Symptoms and Hypotheses 65
Figure 4-1: SALT's Representation of the Links among Knowledge 90
Pieces
Figure 4-2: Knowledge Base Showing Cyclicity in Dependency 94
Figure 4-3: Knowledge Base Modified to Remove Cyclicity 96
Figure 4-4: Knowledge Base Prepared for a Propose-and-Revise 97
Treatment of Cyclicity
Figure 4-5: Knowledge Base with Antagonistic Constraints 102
Figure 5-1: Overview 129
Figure 5-2: KNACK's Approach 135
Figure 5-3: Abstract Domain Model 139
Figure 5-4: Preliminary Domain Model --Concepts 140
Figure 5-5: Domain Model --Structural Knowledge 141
Figure 5-6: Domain Model --Functional Knowledge 142
Figure 7-1: RIME Overview Diagram 207
Figure 7-2: Sample Subgroup Scheme Definition Guides 219
List of Tables
Table 5-1: Enclosure Concept 140
Table 5-2: Antenna Concept 155
Table 5-3: Subsystem Concept 163
Table 5-4: Complexity of the Domain 170
Table 5-5: The Knowledge Base 170
Table 5-6: Effort 172
Contributing Authors
Judith Bachant
AI Research Center
Digital Equipment Corporation
290 Donald Lynch Boulevard, DLB5-3/B3
Marlborough, Massachusetts 01752
Larry Eshelman
Philips Laboratories
345 Scarborough Road
Briarcliff Manor, New York 10510
Gary Kahn
Carnegie Group, Inc.
5 PPG Place
Pittsburgh, Pennsylvania 15222
Georg Klinker
Department of Computer Science
Carnegie Mellon University
Pittsburgh, Pennsylvania 15213
Sandra Marcus
Knowledge Systems Laboratory
Advanced Technology Center
Boeing Computer Services
P.O. Box 24346, MIS 7L-64
Seattle, Washington 98124
John McDermott
AI Research Center
Digital Equipment Corporation
290 Donald Lynch Boulevard, DLB5-3/E2
Marlborough, Massachusetts 01752
Daniel Offutt
Electrical Engineering and Computer Science Department
University of Michigan
Ann Arbor, Michigan 48109