Artificial intelligence: Structure and stretegies for complex problem solving

KEY MESSAGE: 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 TOPICS: 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. MARKET: For all readers interested in artificial intelligence. Cover......Page 1 Contents......Page 20 Preface......Page 8 Publisher’s Acknowledgements......Page 17 PART I: ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE......Page 26 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice......Page 28 1.2 Overview of AI Application Areas......Page 45 1.3 Artificial Intelligence—A Summary......Page 55 1.4 Epilogue and References......Page 56 1.5 Exercises......Page 58 PART II: ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH......Page 60 2.1 The Propositional Calculus......Page 70 2.2 The Predicate Calculus......Page 75 2.3 Using Inference Rules to Produce Predicate Calculus Expressions......Page 87 2.4 Application: A Logic-Based Financial Advisor......Page 98 2.6 Exercises......Page 102 3.0 Introduction......Page 104 3.1 Graph Theory......Page 107 3.2 Strategies for State Space Search......Page 118 3.3 Using the State Space to Represent Reasoning with the Predicate Calculus......Page 132 3.5 Exercises......Page 146 4.0 Introduction......Page 148 4.1 Hill Climbing and Dynamic Programming......Page 152 4.2 The Best-First Search Algorithm......Page 158 4.3 Admissibility, Monotonicity, and Informedness......Page 170 4.4 Using Heuristics in Games......Page 175 4.5 Complexity Issues......Page 182 4.6 Epilogue and References......Page 186 4.7 Exercises......Page 187 5.0 Introduction......Page 190 5.1 The Elements of Counting......Page 192 5.2 Elements of Probability Theory......Page 195 5.3 Applications of the Stochastic Methodology......Page 207 5.4 Bayes’ Theorem......Page 211 5.5 Epilogue and References......Page 215 5.6 Exercises......Page 216 6.0 Introduction......Page 218 6.1 Recursion-Based Search......Page 219 6.2 Production Systems......Page 225 6.3 The Blackboard Architecture for Problem Solving......Page 242 6.4 Epilogue and References......Page 244 6.5 Exercises......Page 245 PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE......Page 248 7.0 Issues in Knowledge Representation......Page 252 7.1 A Brief History of AI Representational Systems......Page 253 7.2 Conceptual Graphs: A Network Language......Page 273 7.3 Alternative Representations and Ontologies......Page 283 7.4 Agent Based and Distributed Problem Solving......Page 290 7.5 Epilogue and References......Page 295 7.6 Exercises......Page 298 8.0 Introduction......Page 302 8.1 Overview of Expert System Technology......Page 304 8.2 Rule-Based Expert Systems......Page 311 8.3 Model-Based, Case Based, and Hybrid Systems......Page 323 8.4 Planning......Page 339 8.5 Epilogue and References......Page 354 8.6 Exercises......Page 356 9.0 Introduction......Page 358 9.1 Logic-Based Abductive Inference......Page 360 9.2 Abduction: Alternatives to Logic......Page 375 9.3 The Stochastic Approach to Uncertainty......Page 388 9.4 Epilogue and References......Page 404 9.5 Exercises......Page 406 PART IV: MACHINE LEARNING......Page 410 10.0 Introduction......Page 412 10.1 A Framework for Symbol-based Learning......Page 415 10.2 Version Space Search......Page 421 10.3 The ID3 Decision Tree Induction Algorithm......Page 433 10.4 Inductive Bias and Learnability......Page 442 10.5 Knowledge and Learning......Page 447 10.6 Unsupervised Learning......Page 458 10.7 Reinforcement Learning......Page 467 10.8 Epilogue and References......Page 474 10.9 Exercises......Page 475 11.0 Introduction......Page 478 11.1 Foundations for Connectionist Networks......Page 480 11.2 Perceptron Learning......Page 483 11.3 Backpropagation Learning......Page 492 11.4 Competitive Learning......Page 499 11.5 Hebbian Coincidence Learning......Page 509 11.6 Attractor Networks or “Memories”......Page 520 11.7 Epilogue and References......Page 530 11.8 Exercises......Page 531 12.0 Genetic and Emergent Models of Learning......Page 532 12.1 The Genetic Algorithm......Page 534 12.2 Classifier Systems and Genetic Programming......Page 544 12.3 Artificial Life and Society-Based Learning......Page 555 12.4 Epilogue and References......Page 566 12.5 Exercises......Page 567 13.0 Stochastic and Dynamic Models of Learning......Page 568 13.1 Hidden Markov Models (HMMs)......Page 569 13.2 Dynamic Bayesian Networks and Learning......Page 579 13.3 Stochastic Extensions to Reinforcement Learning......Page 589 13.4 Epilogue and References......Page 593 13.5 Exercises......Page 595 PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING......Page 598 14.0 Introduction to Weak Methods in Theorem Proving......Page 600 14.1 The General Problem Solver and Difference Tables......Page 601 14.2 Resolution Theorem Proving......Page 607 14.3 PROLOG and Automated Reasoning......Page 628 14.4 Further Issues in Automated Reasoning......Page 634 14.6 Exercises......Page 642 15.0 The Natural Language Understanding Problem......Page 644 15.1 Deconstructing Language: An Analysis......Page 647 15.2 Syntax......Page 650 15.3 Transition Network Parsers and Semantics......Page 658 15.4 Stochastic Tools for Language Understanding......Page 674 15.5 Natural Language Applications......Page 683 15.6 Epilogue and References......Page 691 15.7 Exercises......Page 692 PART VI: EPILOGUE......Page 696 16.0 Introduction......Page 698 16.1 Artificial Intelligence: A Revised Definition......Page 700 16.2 The Science of Intelligent Systems......Page 713 16.3 AI: Current Challanges and Future Directions......Page 723 16.4 Epilogue and References......Page 728 Bibliography......Page 730 B......Page 760 F......Page 761 I......Page 762 M......Page 763 P......Page 764 S......Page 765 Z......Page 766 A......Page 768 C......Page 769 D......Page 770 G......Page 771 H......Page 772 M......Page 773 O......Page 775 P......Page 776 S......Page 777 W......Page 778 X......Page 779

Artificial Intelligence

Structures and Strategies for Complex Problem Solving, Sixth Edition

by George F Luger

This accessible, comprehensive book captures the essence of artificial intelligence -- solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning.

This book is ideal for a one - or two-semester university course on AI.

New to this edition:

• A new chapter on stochastic approaches to machine learning, including first-prder Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation.
• 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.
• Presentation of agent technology and the use of ontologies.
• Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi.
• A new supplemental programming book is available: AI Algorithms in Prolog, Lisp, and JavaTM. Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.

"There are many ideas in this area that students often find difficult; the clarity and precision of Luger's exposition is informed by sharp, incisive examples with straightforward graphical components."

-- Joseph Lewis, San Diego State University

"The book is a perfect complement to an AI course. It gives readers both an historical point of view and a practical guide to all the techniques. It is THE book I would recommend as an introduction to this field."

-- Pascal Rebreyend, Dalarna University

"The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature."

-- Malachy Eaton, University of Limerick

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his Ph.D. from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

"Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two semester university course on AI, as well as an invaluable reference for researchers in the field or practitioners wishing to employ the power of current AI techniques in their work."--BOOK JACKET. **KEY MESSAGE:****KEY TOPICS:**__AI Algorithms in Prolog, Lisp and Java (TM).__**MARKET:**