000			06182nam a2200205 4500
020			_a9788120307773
040			_cCUS
082			_a006.338 _bPAT/I
100			_aPatterson, Dan W.
245			_aIntroduction to artificial intelligence and expert systems _cDan W. Patterson
260			_aNew Delhi: _bPHI Learning, _c2010.
300			_axv, 448 p. _bill.
504			_aIncludes references and index
505			_aPart 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
650			_aArtificial intelligence
650			_aKnowledge representation
650			_aExpert system
650			_aNatural language processing
942			_cL2C2
999			_c3376 _d3376