Artificial Intelligence

Structures and Strategies for Complex Problem Solving

Paperback Engels 2008 9780321545893
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Samenvatting

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 Java™. 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.

Specificaties

ISBN13:9780321545893
Taal:Engels
Bindwijze:Paperback

Lezersrecensies

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Inhoudsopgave

<ul> <li> <div style="MARGIN: 0px"> <strong>PART I: ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE</strong> </div> </li> <li> <div style="MARGIN: 0px"> 1 AI: HISTORY AND APPLICATIONS </div> </li> <li> <div style="MARGIN: 0px"> <strong>PART II: ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH</strong> </div> </li> <li> <div style="MARGIN: 0px"> 2 THE PREDICATE CALCULUS </div> </li> <li> <div style="MARGIN: 0px"> 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH </div> </li> <li> <div style="MARGIN: 0px"> 4 HEURISTIC SEARCH </div> </li> <li> <div style="MARGIN: 0px"> 5 STOCHASTIC METHODS </div> </li> <li> <div style="MARGIN: 0px"> 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH </div> </li> <li> <div style="MARGIN: 0px"> <strong>PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE</strong> </div> </li> <li> <div style="MARGIN: 0px"> 7 KNOWLEDGE REPRESENTATION </div> </li> <li> <div style="MARGIN: 0px"> 8 STRONG METHOD PROBLEM SOLVING </div> </li> <li> <div style="MARGIN: 0px"> 9 REASONING IN UNCERTAIN SITUATIONS </div> </li> <li> <div style="MARGIN: 0px"> <strong>PART IV: MACHINE LEARNING</strong> </div> </li> <li> <div style="MARGIN: 0px"> 10 MACHINE LEARNING: SYMBOL-BASED </div> </li> <li> <div style="MARGIN: 0px"> 11 MACHINE LEARNING: CONNECTIONIST </div> </li> <li> <div style="MARGIN: 0px"> 12 MACHINE LEARNING: GENETIC AND EMERGENT </div> </li> <li> <div style="MARGIN: 0px"> 13 MACHINE LEARNING: PROBABILISTIC </div> </li> <li> <div style="MARGIN: 0px"> <strong>PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING</strong> </div> </li> <li> <div style="MARGIN: 0px"> 14 AUTOMATED REASONING </div> </li> <li> <div style="MARGIN: 0px"> 15 UNDERSTANDING NATURAL LANGUAGE </div> </li> <li> <div style="MARGIN: 0px"> <strong>PART VI</strong> </div> </li> <li> <div style="MARGIN: 0px"> 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY </div> </li> <li> <div style="MARGIN: 0px"> Bibliography </div> </li> <li> <div style="MARGIN: 0px"> Author Index </div> </li> <li> <div style="MARGIN: 0px"> Subject Index <br> </div> </li> </ul>

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