Introduction to artificial intelligence and expert systems /
Patterson, Dan W.,
Introduction to artificial intelligence and expert systems / Dan W. Patterson. - Englewood Cliffs, N.J. : Prentice Hall, c1990. - xv, 448 p. ill. ; 25 cm.
Cover title: Introduction to artificial intelligence & expert systems. Includes index.
Bibliography: p. 432-440.
Part 1 Introduction to Artificial Intelligence
1 OVERVIEW OF ARTIFICIAL INTELUGENCE
1.1 What is AI?
1.2 The Importance of AI
1.3 Early Work in AI
1.4 AI and Related Fields
1.5 Summary
2 KNOWLEOGE: GENERAL CONCEPTS
2.1 Introduction
2.2 Definition and Importance of Knowledge
2.3 Knowledge-Based Systems
2.4 Representation of Knowledge
2.5 Knowledge Organization
2.6 Knowledge Manipulation
2.7 Acquisition of Knowledge
2.8 Summary
3 LISP AND OTHER Al PROGRAMMING LANGUAGES
3.1 Introduction to LISP: Syntax and Numeric
Functions
3.2 Basic List Manipulation Functions in LISP
3.3 Functions, Predicates, and Conditionals
3.4 Input, Output, and Local Variables
3.5 Iteration and Recursion
3.6 Property Lists and Arrays
3.7 Miscellaneous Topics
3.8 PROLOG and Other AI Programming Languages
3.9 Summary 43
Part 2 Knowledge Representation
4 FORMALIZED SYMBOLIC LOGICS
4.1 Introduction
4.2 Syntax and Semantics for Propositional Logic
4.3 Syntax and Semantics for FOPL
4.4 Properties of Wffs
4.5 Conversion to Clausal Form
4.6 Inference Rules
4.7 The Resolution Principle
4.8 Nondeductive Inference Methods
Contents
4.9 Representations Using Rules 75
4.10 Summary 76
Exercises 77
5 DEALING WITH INCONSISTENCIES AND UNCERTAINTIES 8
5.1 Introduction
5.2 Truth Maintenance Systems
5.3 Default Reasoning and the Closed World
Assumption
5.4 Predicate Completion and Circumscription
5.5 Modal and Temporal Logics
5.6 Fuzzy Logic and Natural Language Computations
5.7 Summary
6 PROBABILISTIC REASONING
6.1 Introduction
6.2 Bayesian Probabilistic Inference
6.3 Possible World Representations
6.4 Dempster-Shafer Theory
6.5 Ad-Hoc Methods
6.6 Heuristic Reasoning Methods
6.7 Summary
7 STRUCTURED KNOWLEDGE: GRAPHS, FRAMES, AND
RELATED STRUCTURES
7.1 Introduction
7.2 Associative Networks
7.3 Frame Structures
7.4 Conceptual Dependencies and Scripts
VIII
7.5 Summary 1
8 OBJECT-ORIENTED REPRESENTATIONS
8.1 Introduction
8.2 Overview of Object-Oriented Systems
8.3 Objects, Classes, Messages, and Methods
8.4 Simulation Example Using an OOS Program
8.5 Object Oriented Languages and Systems
8.6 Summary
Part 3 Knowledge Organization and Manipulation
9 SEARCH AND CONTRDL STRATEGIES
9.1 Introduction
9.2 Preliminary Concepts
9.3 Examples of Search Problems
9.4 Uniformed or Blind Search
9.5 Informed Search
9.6 Searching And-Or Graphs
9.7 Summary
10 MATCHING TECHNIQUES
10.1 Introduction
10.2 Structures Used in Matching
10.3 Measures for Matching
10.4 Matching Like Patterns
10.5 Partial Matching
10.6 Fuzzy Matching Algorithms
10.7 The RETE Matching Algorithm
10.8 Summary 209
Exercises 209
i 1 KNOWLEDGE ORGANIZATION 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, CommunicatlDn, 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
15 GENERAL CDNCEPTS IN KNOWLEDGE ACQUISITION
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
t7 EARLY WORK IN MACHtNE LEARNING
17.1 Introduction 367
17.2 Perceptions 368
17.3 Checker Playing Example 370
17.4 Learning Automata 372
17.5 Genetic Algorithms 375
17.6 Intelligent Editors 378
17.7 Sununary 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
19 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
20 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
9788120307773 (pb)
Artificial intelligence.
Expert systems (Computer science)
006.338 / PAT/I
Introduction to artificial intelligence and expert systems / Dan W. Patterson. - Englewood Cliffs, N.J. : Prentice Hall, c1990. - xv, 448 p. ill. ; 25 cm.
Cover title: Introduction to artificial intelligence & expert systems. Includes index.
Bibliography: p. 432-440.
Part 1 Introduction to Artificial Intelligence
1 OVERVIEW OF ARTIFICIAL INTELUGENCE
1.1 What is AI?
1.2 The Importance of AI
1.3 Early Work in AI
1.4 AI and Related Fields
1.5 Summary
2 KNOWLEOGE: GENERAL CONCEPTS
2.1 Introduction
2.2 Definition and Importance of Knowledge
2.3 Knowledge-Based Systems
2.4 Representation of Knowledge
2.5 Knowledge Organization
2.6 Knowledge Manipulation
2.7 Acquisition of Knowledge
2.8 Summary
3 LISP AND OTHER Al PROGRAMMING LANGUAGES
3.1 Introduction to LISP: Syntax and Numeric
Functions
3.2 Basic List Manipulation Functions in LISP
3.3 Functions, Predicates, and Conditionals
3.4 Input, Output, and Local Variables
3.5 Iteration and Recursion
3.6 Property Lists and Arrays
3.7 Miscellaneous Topics
3.8 PROLOG and Other AI Programming Languages
3.9 Summary 43
Part 2 Knowledge Representation
4 FORMALIZED SYMBOLIC LOGICS
4.1 Introduction
4.2 Syntax and Semantics for Propositional Logic
4.3 Syntax and Semantics for FOPL
4.4 Properties of Wffs
4.5 Conversion to Clausal Form
4.6 Inference Rules
4.7 The Resolution Principle
4.8 Nondeductive Inference Methods
Contents
4.9 Representations Using Rules 75
4.10 Summary 76
Exercises 77
5 DEALING WITH INCONSISTENCIES AND UNCERTAINTIES 8
5.1 Introduction
5.2 Truth Maintenance Systems
5.3 Default Reasoning and the Closed World
Assumption
5.4 Predicate Completion and Circumscription
5.5 Modal and Temporal Logics
5.6 Fuzzy Logic and Natural Language Computations
5.7 Summary
6 PROBABILISTIC REASONING
6.1 Introduction
6.2 Bayesian Probabilistic Inference
6.3 Possible World Representations
6.4 Dempster-Shafer Theory
6.5 Ad-Hoc Methods
6.6 Heuristic Reasoning Methods
6.7 Summary
7 STRUCTURED KNOWLEDGE: GRAPHS, FRAMES, AND
RELATED STRUCTURES
7.1 Introduction
7.2 Associative Networks
7.3 Frame Structures
7.4 Conceptual Dependencies and Scripts
VIII
7.5 Summary 1
8 OBJECT-ORIENTED REPRESENTATIONS
8.1 Introduction
8.2 Overview of Object-Oriented Systems
8.3 Objects, Classes, Messages, and Methods
8.4 Simulation Example Using an OOS Program
8.5 Object Oriented Languages and Systems
8.6 Summary
Part 3 Knowledge Organization and Manipulation
9 SEARCH AND CONTRDL STRATEGIES
9.1 Introduction
9.2 Preliminary Concepts
9.3 Examples of Search Problems
9.4 Uniformed or Blind Search
9.5 Informed Search
9.6 Searching And-Or Graphs
9.7 Summary
10 MATCHING TECHNIQUES
10.1 Introduction
10.2 Structures Used in Matching
10.3 Measures for Matching
10.4 Matching Like Patterns
10.5 Partial Matching
10.6 Fuzzy Matching Algorithms
10.7 The RETE Matching Algorithm
10.8 Summary 209
Exercises 209
i 1 KNOWLEDGE ORGANIZATION 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, CommunicatlDn, 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
15 GENERAL CDNCEPTS IN KNOWLEDGE ACQUISITION
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
t7 EARLY WORK IN MACHtNE LEARNING
17.1 Introduction 367
17.2 Perceptions 368
17.3 Checker Playing Example 370
17.4 Learning Automata 372
17.5 Genetic Algorithms 375
17.6 Intelligent Editors 378
17.7 Sununary 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
19 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
20 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
9788120307773 (pb)
Artificial intelligence.
Expert systems (Computer science)
006.338 / PAT/I