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)