Introduction to artificial intelligence and expert systems Dan W. Patterson

Includes references and index

Part 1 Introduction to Artificial intelligence 1
1 OVERVIEW OF ARTIFICIAL INTELUGENCE 1
1.1 What is AI? 2
1.2 The Importance of AI 3
1.3 Early Work in AI 5
1.4 AI and Related Fields 7
1.5 Summary 8
2 KNOWLEDGE: GENERAL CONCEPTS 9
2.1 Introduction 9
2.2 Definition and Importance of Knowledge 10
2.3 Knowledge-Based Systems 13
2.4 Representation of Knowledge 14
2.5 Knowledge Organization 16
2.6 Knowledge Manipulation 16
2.7 Acquisition of Knowledge 17
2.8 Sununaiy 17
Exercises 17
3 USP AND OTHER Al PROGRAMMING LANGUAGES
3.1 Introduction to LISP: Syntax and Numeric
Functions 19
3.2 Basic List Manipulation Functions in LISP 22
3.3 Functions, Predicates, and Conditionals 25
3.4 Input, Output, and Local Variables 29
3.5 Iteration and Recursion 33
3.6 Property Lists and Arrays 35
3.7 Miscellaneous Topics 38
3.8 PROLOG and Other Al Programming Languages 40
3.9 Summary 43
Exercises 44
Part 2 Knowledge Representatien
4 FORMALIZED SYMBOUC LOGICS
4.1 Introduction 47
4.2 Syntax and Semantics for Prepositional Logic 49
4.3 Syntax and Semantics for FOPL 55
4.4 Properties of WfFs 60
4.5 Conversion to Clausal Form 62
4.6 Inference Rules 65
4.7 The Resolution Principle 66
4 8 Nondeductive Inference Methods 73
. 4.9 Representations Using Rules 75
4.10 Summary 76
Exercises 77
5 DEALING WITH INCONSISTENCIES AND UNCERTAINTIES
5.1 Introduction 81
5.2 Truth Maintenance Systems 82
5.3 Default Reasoning and the Closed World
Assumption 87
5.4 Predicate Completion and Circumscription 90
5.5 Modal and Temporal Logics 92
5.6 Fuzzy Logic and Natural Language Computations 97
5.7 Summary 104
Exercises 105
S PROBABILISTIC REASONING
6.1 Introduction 107
6.2 Bayesian Probabilistic Inference 109
6.3 Possible World Representations 113
6.4 Dempster-Shafer Theory 115
6.5 Ad-Hoc Methods 119
6.6 Heuristic Reasoning Methods 122
6.7 Summary 123
Exercises 124
7 STRUCTURED KNOWLEDGE: GRAPHS, FRAMES. AND
RELATED STRUCTURES
7.1 Introduction 126
7.2 Associative Networks 127
7.3 Frame Structures 136
7.4 Conceptual Dependencies and Scripts 140
7.5 Summary !44
Exercises 145
8 OBJECT-OmENTED REPRESENTATIONS
8.1 Introduction 147
8.2 Overview of Object-Oriented Systems 149
8.3 Objects, Classes, Messages, and Methods 150
8.4 Simulation Example Using an OOS Program 155
8.5 Object Oriented Languages and Systems 161
8.6 Summary 164
Exercises 165
Part 3 Knowledge Organization and Manipulation
9 SEARCH AND CONTROL STRATEGIES
9.1 Introduction 167
9.2 Preliminary Concepts 168
9.3 Examples of Search Problems 170
9.4 Uniformed or Blind Search 174
9.5 Informed Search 178
9.6 Searching And-Or Graphs 184
9.7 Summary 185
Exercises 186
10 MATCHING TECHNIQUES
10.1 Introduction 188
10.2 Structures Used in Matching 191
10.3 Measures for Matching 194
10.4 Matching Like Patterns 198
10.5 Partial Matching 201
10.6 Fuzzy Matching Algorithms 204
10.7 The RETE Matching Algorithm 205
10.8 Summary 209
Exercises 209
11 KNOWLEDGE ORGANIS^ATION AND MANAGEMENT
11.1 Introduction 212
11.2 Indexing and Retrieval Techniques 215
11.3 Integrating Knowledge in Memory 219
11.4 Memory Organization Systems 220
11.5 Summary 225
Exercises 225
Part 4 Perception, Communication, and Expert Systems
12 NATURAL LANGUAGE PROCESSING
12.1 Introduction 228
12.2 Overview of Linguistics 228
12.3 Grammars and Languages 231
12.4 Basic Parsing Techniques 240
12.5 Sematic Analysis and Representation
Structures 255
12.6 Natural Language Generation 259
12.7 Natural Language Systems 264
12.8 Summary 266
Exercises 267
13 PATTERN RECOGNITION
13.1 Introduction 272
13.2 The Recognition and Classification Process 273
13.3 Learning Classification Patterns 277
13.4 Recognizing and Understanding Speech 281
13.5 Summary 282
Exercises 283
14 VISUAL IMAGE UNDERSTANDING
14.1 Introduction 285
14.2 Image Transformation and Low-Level
Processing 290
14.3 Intermediate-Level Image Processing 299
14.4 Describing and Labeling Objects 304
14.5 High-Level Processing 312
14.6 Vision System Architectures 317
14.7 Summary 323
Exercises 323
15 EXPERT SYSTEMS ARCHITECTURES
15.1 Introduction 327
15.2 Rule-Based System Architectures 330
15.3 Nonproduction System Architectures 337
15.4 Dealing with Uncertainty 347
15.5 Knowledge Acquisition and Validation 347
15.6 Knowledge System Building Tools 349
15.7 Summary 354
Exercises 354
Part 5 Knowledge Acquisition
16 GENERAL CONCEPTS IN KNOWLEDGE ACGUISITION
16.1 Introduction 357
16.2 Types of Learning 359
16 3 Knowledge Acquisition Is Difficult 360
16.4 General Learning Model 361
16.5 Performance Measures 364
16.6 Summary 365
Exercises 366
17 EARLY WORK IN MACHINE LEARNING
17.1 Introduction 367
17.2 Perceptrons 368
17.3 Checker Playing Example 370
17.4 Learning Automata 372
17.5 Genetic Algorithms 375
17.6 Intelligent Editors 378
17.7 Summary 379
Exercises 379
18 LEARNING BY INDUCTION
18.1 Introduction 381
18.2 Basic Concepts 382
18.3 Some Definitions 383
18.4 Generalization and Specialization 385
18.5 Inductive Bias 388
18.6 Example of an Inductive Learner 390
18.7 Summary 398
Exercises 399
13 EXAMPLES OF OTHER INDUCTIVE LEARNERS
19.1 Introduction 401
19.2 The ID3 System 401
19.3 The LEX System 405
19.4 The INDUCE System 409
19.5 Learning Structure Concepts 412
19.6 Summary 413
Exercises 414
so ANALOGICAL AND EXPLANATION-BASED LEARNING
20.1 Introduction 416
20.2 Analogical Reasoning and Learning 417
20.3 Examples of Analogical Learning Systems 421
20.4 Explanation-Based Learning 426
20.5 Summary 430
Exercises 431