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09791nam a22002897i 4500 |
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20230824143358.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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071207s2009 maua f b 001 0 eng d |
| 040 ## - CATALOGING SOURCE |
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EG-NcFUE |
| Description conventions |
rda |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
22 |
| Classification number |
006.3 |
| Item number |
L.G.A |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Luger, George F. |
| 9 (RLIN) |
9845 |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
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| Title |
Artificial intelligence : |
| Remainder of title |
structures and strategies for complex problem solving / |
| Statement of responsibility, etc |
George F. Luger. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
Fifteenth edition |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New Delhi : |
| Name of publisher, distributor, etc |
Pearson Addison-Wesley, |
| Date of publication, distribution, etc |
[2013] |
| 264 #0 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
©2013. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxiii, 903 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
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rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
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rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
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rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references (p. 705-733) and indexes. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Summary Already in its fifth edition, thanks to artificial intelligence (AI) being one of the most volatile fields in computer science, this text is designed for the classroom as well as a reference tool. Luger (computer science, linguistics, and technology, U. of New Mexico) describes the aspects of AI, including representation and search (including the predicate calculus, and heuristic and stochastic search methods), representation and intelligence (including knowledge-intensive and strong methods), machine learning (symbol- based, connectionist, social and emergent), problem solving (automated reasoning and natural language), language and programming techniques, and a closing essay on AI as empirical inquiry. Luger includes exercises and references for each chapter. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Author Biography George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico in Albuquerque |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Table of Contents Preface p. vii Publisher's Acknowledgements p. xv Part I Artificial Intelligence: Its Roots and Scope p. 1 1 Al: History and Applications p. 3 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice p. 3 1.2 Overview of AI Application Areas p. 17 1.3 Artificial Intelligence--A Summary p. 28 1.4 Epilogue and References p. 29 1.5 Exercises p. 31 Part II Artificial Intelligence as Representation and Search p. 33 2 The Predicate Calculus p. 47 2.0 Introduction p. 47 2.1 The Propositional Calculus p. 47 2.2 The Predicate Calculus p. 52 2.3 Using Inference Rules to Produce Predicate Calculus Expressions p. 64 2.4 Application: A Logic-Based Financial Advisor p. 75 2.5 Epilogue and References p. 79 2.6 Exercises p. 79 3 Structures and Strategies for State Space Search p. 81 3.0 Introduction p. 81 3.1 Graph Theory p. 84 3.2 Strategies for State Space Search p. 93 3.3 Using the State Space to Represent Reasoning with the Predicate Calculus p. 107 3.4 Epilogue and References p. 121 3.5 Exercises p. 121 4 Heuristic Search p. 123 4.0 Introduction p. 123 4.1 An Algorithm for Heuristic Search p. 127 4.2 Admissibility, Monotonicity, and Informedness p. 139 4.3 Using Heuristics in Games p. 144 4.4 Complexity Issues p. 152 4.5 Epilogue and References p. 156 4.6 Exercises p. 156 5 Control and Implementation of State Space Search p. 159 5.0 Introduction p. 159 5.1 Recursion-Based Search p. 160 5.2 Pattern-Directed Search p. 164 5.3 Production Systems p. 171 5.4 The Blackboard Architecture for Problem Solving p. 187 5.5 Epilogue and References p. 189 5.7 Exercises p. 190 Part III Representation and Intelligence: The AI Challenge p. 193 6 Knowledge Representation p. 197 6.0 Issues in Knowledge Representation p. 197 6.1 A Brief History of AI Representational Systems p. 198 6.2 Conceptual Graphs: A Network Language p. 218 6.3 Alternatives to Explicit Representation p. 228 6.4 Agent Based and Distributed Problem Solving p. 235 6.5 Epilogue and References p. 240 6.6 Exercises p. 243 7 Strong Method Problem Solving p. 247 7.0 Introduction p. 247 7.1 Overview of Expert System Technology p. 249 7.2 Rule-Based Expert Systems p. 256 7.3 Model-Based, Case Based, and Hybrid Systems p. 268 7.4 Planning p. 284 7.5 Epilogue and References p. 299 7.6 Exercises p. 301 8 Reasoning in Uncertain Situations p. 303 8.0 Introduction p. 303 8.1 Logic-Based Abductive Inference p. 305 8.2 Abduction: Alternatives to Logic p. 320 8.3 The Stochastic Approach to Uncertainty p. 333 8.4 Epilogue and References p. 344 8.5 Exercises p. 346 Part IV Machine Learning p. 349 9 Machine Learning: Symbol-based p. 351 9.0 Introduction p. 603 9.1 A Framework for Symbol-based Learning p. 354 9.2 Version Space Search p. 360 9.3 The ID3 Decision Tree Induction Algorithm p. 372 9.4 Inductive Bias and Learnability p. 381 9.5 Knowledge and Learning p. 386 9.6 Unsupervised Learning p. 397 9.7 Reinforcement Learning p. 406 9.8 Epilogue and References p. 413 9.9 Exercises p. 414 10 Machine Learning: Connectionist p. 417 10.0 Introduction p. 417 10.1 Foundations for Connectionist Networks p. 419 10.2 Perceptron Learning p. 422 10.3 Backpropagation Learning p. 431 10.4 Competitive Learning p. 438 10.5 Hebbian Coincidence Learning p. 446 10.6 Attractor Networks or "Memories" p. 457 10.7 Epilogue and References p. 467 10.8 Exercises p. 468 11 Machine Learning: Social and Emergent p. 469 11.0 Social and Emergent Models of Learning p. 469 11.1 The Genetic Algorithm p. 471 11.2 Classifier Systems and Genetic Programming p. 481 11.3 Artificial Life and Society-Based Learning p. 492 11.4 Epilogue and References p. 503 11.5 Exercises p. 504 Part V Advanced Topics for AI Problem Solving p. 507 12 Automated Reasoning p. 509 12.0 Introduction to Weak Methods in Theorem Proving p. 509 12.1 The General Problem Solver and Difference Tables p. 510 12.2 Resolution Theorem Proving p. 516 12.3 PROLOG and Automated Reasoning p. 537 12.4 Further Issues in Automated Reasoning p. 543 12.5 Epilogue and References p. 550 12.6 Exercises p. 551 13 Understanding Natural Language p. 553 13.0 Role of Knowledge in Language Understanding p. 553 13.1 Deconstructing Language: A Symbolic Analysis p. 556 13.2 Syntax p. 559 13.3 Syntax and Knowledge with ATN Parsers p. 568 13.4 Stochastic Tools for Language Analysis p. 578 13.5 Natural Language Applications p. 585 13.6 Epilogue and References p. 592 13.7 Exercises p. 557 Part VI Languages and Programming Techniques for Artificial Intelligence p. 597 14 An Introduction to Prolog p. 603 14.0 Introduction p. 603 14.1 Syntax for Predicate Calculus Programming p. 604 14.2 Abstract Data Types (ADTs) in PROLOG p. 616 14.3 A Production System Example in PROLOG p. 620 14.4 Designing Alternative Search Strategies p. 625 14.5 A PROLOG Planner p. 630 14.6 PROLOG: Meta-Predicates, Types, and Unification p. 633 14.7 Meta-Interpreters in PROLOG p. 641 14.8 Learning Algorithms in PROLOG p. 656 14.9 Natural Language Processing in PROLOG p. 666 14.10 Epilogue and References p. 673 14.11 Exercises p. 676 15 An Introduction to LISP p. 679 15.0 Introduction p. 679 15.1 LISP: A Brief Overview p. 680 15.2 Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem p. 702 15.3 Higher-Order Functions and Procedural Abstraction p. 707 15.4 Search Strategies in LISP p. 711 15.5 Pattern Matching in LISP p. 715 15.6 A Recursive Unification Function p. 717 15.7 Interpreters and Embedded Languages p. 721 15.8 Logic Programming in LISP p. 723 15.9 Streams and Delayed Evaluation p. 732 15.10 An Expert System Shell in LISP p. 736 15.11 Semantic Networks and Inheritance in LISP p. 743 15.12 Object-Oriented Programming Using CLOS p. 747 15.13 Learning in LISP: The ID3 Algorithm p. 759 15.14 Epilogue and References p. 771 15.15 Exercises p. 772 Part VII Epilogue p. 777 16 Artificial Intelligence as Empirical Enquiry p. 779 16.0 Introduction p. 779 16.1 Artificial Intelligence: A Revised Definition p. 781 16.2 The Science of Intelligent Systems p. 792 16.3 AI: Current Issues and Future Directions p. 803 16.4 Epilogue and References p. 807 Bibliography p. 809 Author Index p. 837 Subject Index p. 843 |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence-solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: "AI Algorithms in Prolog, Lisp and Java (TM). "References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |