Knowledge Acquisition for Expert Systems

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Even the brightest artificial intelligence must be educated before it is much help. Here is a guide for developers of expert systems to eliciting and organizing human expertise in such a way that the machine can comprehend and use it. Updated from the 1989 first edition. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780070269118
  • Publisher: McGraw-Hill Companies, The
  • Publication date: 3/1/1992
  • Edition description: 2nd ed
  • Edition number: 2

Table of Contents

Preface 9
1 The Nature of Expertise 13
Intelligence 13
Experts 14
The role of experts 16
True expertise 18
Summary 19
2 Programs as Experts 21
Definition of expert systems 21
Advantages of using expert systems 23
How are expert systems different 24
History of expert system development 26
Applications of expert systems and some examples 27
Problems of using expert systems 28
Summary 31
3 Systems Analysis--A Comparison 32
Overview of systems analysis 32
Expert system development 35
Knowledge elicitation--a more difficult problem 39
Prototyping 39
A methodology 41
Summary 42
4 The Knowledge Engineer 43
The role of the systems analyst 43
The role of the knowledge engineer 44
Running the project 47
Summary 53
5 Interviewing the Experts 54
General 54
Objectives 58
Examples of output 60
Feedback 64
Methods of questioning 67
Methods from psychology 70
Which methods? 71
Analysis 72
Protocol analysis 76
Other methods 79
Debugging 79
Summary 80
6 Reasoning and Probability Theory 81
The foundations of probability 82
Combination of events 84
Probability distributions 86
Statistical tests 90
Correlation 91
Bayes theorem 92
Problems of estimation 95
Summary 96
7 Fuzziness in Reasoning 98
Usage of words 98
Fuzziness 99
IF ... THEN rules 100
Symptoms 103
Weak rules 106
Incompatibility 107
Analogies 108
Uncertainty in data 109
Endorsements 111
Fuzzy logic 112
Fuzzy sets 112
Applications of fuzzy logic 116
Other forms of logic 117
Summary 118
8 Machine Induction 119
Principles of induction 119
Requirements for induction 120
ID3 rule induction 125
A simple case study 126
Analysis of case study 130
Results from taxonomy 135
Problems with uncertain data 137
General comments 140
The structured approach 144
Other algorithms 144
Neural networks 144
Summary 145
9 The Repertory Grid 147
Personal construct theory 147
The grid 148
Grid elicitation 151
Grid analysis 152
A case study in repertory grid 152
The grid's usefulness 161
Summary 162
10 Try It Anyway--Case Studies 166
Attersley Vegetable Research Station 166
Cotting Hospital 172
United Chemists 175
Overview 177
Finally... 178
References 179
Glossary 185
Index 193
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