Introduction to artificial intelligence and expert systems (Record no. 3376)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06182nam a2200205 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788120307773 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CUS |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.338 |
| Item number | PAT/I |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Patterson, Dan W. |
| 245 ## - TITLE STATEMENT | |
| Title | Introduction to artificial intelligence and expert systems |
| Statement of responsibility, etc. | Dan W. Patterson |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | New Delhi: |
| Name of publisher, distributor, etc. | PHI Learning, |
| Date of publication, distribution, etc. | 2010. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xv, 448 p. |
| Other physical details | ill. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes references and index |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Part 1 Introduction to Artificial intelligence 1<br/>1 OVERVIEW OF ARTIFICIAL INTELUGENCE 1<br/>1.1 What is AI? 2<br/>1.2 The Importance of AI 3<br/>1.3 Early Work in AI 5<br/>1.4 AI and Related Fields 7<br/>1.5 Summary 8<br/>2 KNOWLEDGE: GENERAL CONCEPTS 9<br/>2.1 Introduction 9<br/>2.2 Definition and Importance of Knowledge 10<br/>2.3 Knowledge-Based Systems 13<br/>2.4 Representation of Knowledge 14<br/>2.5 Knowledge Organization 16<br/>2.6 Knowledge Manipulation 16<br/>2.7 Acquisition of Knowledge 17<br/>2.8 Sununaiy 17<br/>Exercises 17<br/>3 USP AND OTHER Al PROGRAMMING LANGUAGES<br/>3.1 Introduction to LISP: Syntax and Numeric<br/>Functions 19<br/>3.2 Basic List Manipulation Functions in LISP 22<br/>3.3 Functions, Predicates, and Conditionals 25<br/>3.4 Input, Output, and Local Variables 29<br/>3.5 Iteration and Recursion 33<br/>3.6 Property Lists and Arrays 35<br/>3.7 Miscellaneous Topics 38<br/>3.8 PROLOG and Other Al Programming Languages 40<br/>3.9 Summary 43<br/>Exercises 44<br/>Part 2 Knowledge Representatien<br/>4 FORMALIZED SYMBOUC LOGICS<br/>4.1 Introduction 47<br/>4.2 Syntax and Semantics for Prepositional Logic 49<br/>4.3 Syntax and Semantics for FOPL 55<br/>4.4 Properties of WfFs 60<br/>4.5 Conversion to Clausal Form 62<br/>4.6 Inference Rules 65<br/>4.7 The Resolution Principle 66<br/>4 8 Nondeductive Inference Methods 73<br/>. 4.9 Representations Using Rules 75<br/>4.10 Summary 76<br/>Exercises 77<br/>5 DEALING WITH INCONSISTENCIES AND UNCERTAINTIES<br/>5.1 Introduction 81<br/>5.2 Truth Maintenance Systems 82<br/>5.3 Default Reasoning and the Closed World<br/>Assumption 87<br/>5.4 Predicate Completion and Circumscription 90<br/>5.5 Modal and Temporal Logics 92<br/>5.6 Fuzzy Logic and Natural Language Computations 97<br/>5.7 Summary 104<br/>Exercises 105<br/>S PROBABILISTIC REASONING<br/>6.1 Introduction 107<br/>6.2 Bayesian Probabilistic Inference 109<br/>6.3 Possible World Representations 113<br/>6.4 Dempster-Shafer Theory 115<br/>6.5 Ad-Hoc Methods 119<br/>6.6 Heuristic Reasoning Methods 122<br/>6.7 Summary 123<br/>Exercises 124<br/>7 STRUCTURED KNOWLEDGE: GRAPHS, FRAMES. AND<br/>RELATED STRUCTURES<br/>7.1 Introduction 126<br/>7.2 Associative Networks 127<br/>7.3 Frame Structures 136<br/>7.4 Conceptual Dependencies and Scripts 140<br/>7.5 Summary !44<br/>Exercises 145<br/>8 OBJECT-OmENTED REPRESENTATIONS<br/>8.1 Introduction 147<br/>8.2 Overview of Object-Oriented Systems 149<br/>8.3 Objects, Classes, Messages, and Methods 150<br/>8.4 Simulation Example Using an OOS Program 155<br/>8.5 Object Oriented Languages and Systems 161<br/>8.6 Summary 164<br/>Exercises 165<br/>Part 3 Knowledge Organization and Manipulation<br/>9 SEARCH AND CONTROL STRATEGIES<br/>9.1 Introduction 167<br/>9.2 Preliminary Concepts 168<br/>9.3 Examples of Search Problems 170<br/>9.4 Uniformed or Blind Search 174<br/>9.5 Informed Search 178<br/>9.6 Searching And-Or Graphs 184<br/>9.7 Summary 185<br/>Exercises 186<br/>10 MATCHING TECHNIQUES<br/>10.1 Introduction 188<br/>10.2 Structures Used in Matching 191<br/>10.3 Measures for Matching 194<br/>10.4 Matching Like Patterns 198<br/>10.5 Partial Matching 201<br/>10.6 Fuzzy Matching Algorithms 204<br/>10.7 The RETE Matching Algorithm 205<br/>10.8 Summary 209<br/>Exercises 209<br/>11 KNOWLEDGE ORGANIS^ATION AND MANAGEMENT<br/>11.1 Introduction 212<br/>11.2 Indexing and Retrieval Techniques 215<br/>11.3 Integrating Knowledge in Memory 219<br/>11.4 Memory Organization Systems 220<br/>11.5 Summary 225<br/>Exercises 225<br/>Part 4 Perception, Communication, and Expert Systems<br/>12 NATURAL LANGUAGE PROCESSING<br/>12.1 Introduction 228<br/>12.2 Overview of Linguistics 228<br/>12.3 Grammars and Languages 231<br/>12.4 Basic Parsing Techniques 240<br/>12.5 Sematic Analysis and Representation<br/>Structures 255<br/>12.6 Natural Language Generation 259<br/>12.7 Natural Language Systems 264<br/>12.8 Summary 266<br/>Exercises 267<br/>13 PATTERN RECOGNITION<br/>13.1 Introduction 272<br/>13.2 The Recognition and Classification Process 273<br/>13.3 Learning Classification Patterns 277<br/>13.4 Recognizing and Understanding Speech 281<br/>13.5 Summary 282<br/>Exercises 283<br/>14 VISUAL IMAGE UNDERSTANDING<br/>14.1 Introduction 285<br/>14.2 Image Transformation and Low-Level<br/>Processing 290<br/>14.3 Intermediate-Level Image Processing 299<br/>14.4 Describing and Labeling Objects 304<br/>14.5 High-Level Processing 312<br/>14.6 Vision System Architectures 317<br/>14.7 Summary 323<br/>Exercises 323<br/>15 EXPERT SYSTEMS ARCHITECTURES<br/>15.1 Introduction 327<br/>15.2 Rule-Based System Architectures 330<br/>15.3 Nonproduction System Architectures 337<br/>15.4 Dealing with Uncertainty 347<br/>15.5 Knowledge Acquisition and Validation 347<br/>15.6 Knowledge System Building Tools 349<br/>15.7 Summary 354<br/>Exercises 354<br/>Part 5 Knowledge Acquisition<br/>16 GENERAL CONCEPTS IN KNOWLEDGE ACGUISITION<br/>16.1 Introduction 357<br/>16.2 Types of Learning 359<br/>16 3 Knowledge Acquisition Is Difficult 360<br/>16.4 General Learning Model 361<br/>16.5 Performance Measures 364<br/>16.6 Summary 365<br/>Exercises 366<br/>17 EARLY WORK IN MACHINE LEARNING<br/>17.1 Introduction 367<br/>17.2 Perceptrons 368<br/>17.3 Checker Playing Example 370<br/>17.4 Learning Automata 372<br/>17.5 Genetic Algorithms 375<br/>17.6 Intelligent Editors 378<br/>17.7 Summary 379<br/>Exercises 379<br/>18 LEARNING BY INDUCTION<br/>18.1 Introduction 381<br/>18.2 Basic Concepts 382<br/>18.3 Some Definitions 383<br/>18.4 Generalization and Specialization 385<br/>18.5 Inductive Bias 388<br/>18.6 Example of an Inductive Learner 390<br/>18.7 Summary 398<br/>Exercises 399<br/>13 EXAMPLES OF OTHER INDUCTIVE LEARNERS<br/>19.1 Introduction 401<br/>19.2 The ID3 System 401<br/>19.3 The LEX System 405<br/>19.4 The INDUCE System 409<br/>19.5 Learning Structure Concepts 412<br/>19.6 Summary 413<br/>Exercises 414<br/>so ANALOGICAL AND EXPLANATION-BASED LEARNING<br/>20.1 Introduction 416<br/>20.2 Analogical Reasoning and Learning 417<br/>20.3 Examples of Analogical Learning Systems 421<br/>20.4 Explanation-Based Learning 426<br/>20.5 Summary 430<br/>Exercises 431 |
| 650 ## - SUBJECT | |
| Keyword | Artificial intelligence |
| 650 ## - SUBJECT | |
| Keyword | Knowledge representation |
| 650 ## - SUBJECT | |
| Keyword | Expert system |
| 650 ## - SUBJECT | |
| Keyword | Natural language processing |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | GN Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Date last checked out | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 22/06/2016 | 006.338 PAT/I | P18679 | 13/02/2019 | 13/02/2019 | General Books |