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Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edition

Published by Pearson (February 26, 2008) © 2009

  • George F. Luger
$186.66

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In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence—solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Students learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists.
Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI.
  • Accessible presentation: The combination of a thorough and balanced treatment of the theoretical foundations of intelligent problem solving with the data structures and algorithms needed for implementation provides a holistic picture for students.
  • AI foundations: A unique discussion of the history of AI and social and the associated philosophical issues is presented in the early chapters.
  • Applied programming languages: Example programs are written in three programming languages, Prolog, Lisp, and Java
  • Applications in context: The practical applications of AI are put into context using model-based reasoning and planning examples from the NASA space program. Comments on the AI endeavor from the perspectives of philosophy, psychology and neuro-physiology give students a holistic picture of AI’s application in the real world.
  • Coverage of the stochastic methodology:
    • Stochastic natural language processing, including finite state machines, dynamic programming, and the Viterbi algorithm, is integrated into introductory chapters.
    • Expanded stochastic approaches to reasoning in uncertain situations, including Bayesian belief networks and Markov models, are discussed in Chapter 9.
    • New for the 6th Edition, Chapter 13, Probabilistically Based Machine Learning, covers stochastic methods that support machine learning.
  • Presentation of agent technology and the use of ontologies are added to Chapter 7, Knowledge Presentation.
  • A new machine-learning chapter, based on stochastic methods, Chapter 13, Probabilistically-Based Machine Learning. This new chapter covers stochastic approaches to machine learning, including first-order 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.
  • Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added to Chapter 15, Understanding Natural Language.
  • A new supplemental programming book is available: AI Algorithms in Prolog, Lisp and Java â„¢. Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.
  • References and citations are updated throughout.

  • PART I: ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE
  • 1 AI: HISTORY AND APPLICATIONS
  • PART II: ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH
  • 2 THE PREDICATE CALCULUS
  • 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH
  • 4 HEURISTIC SEARCH
  • 5 STOCHASTIC METHODS
  • 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH
  • PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE
  • 7 KNOWLEDGE REPRESENTATION
  • 8 STRONG METHOD PROBLEM SOLVING
  • 9 REASONING IN UNCERTAIN SITUATIONS
  • PART IV: MACHINE LEARNING
  • 10 MACHINE LEARNING: SYMBOL-BASED
  • 11 MACHINE LEARNING: CONNECTIONIST
  • 12 MACHINE LEARNING: GENETIC AND EMERGENT
  • 13 MACHINE LEARNING: PROBABILISTIC
  • PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING
  • 14 AUTOMATED REASONING
  • 15 UNDERSTANDING NATURAL LANGUAGE
  • PART VI
  • 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY
  • Bibliography
  • Author Index
  • Subject Index
George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his PhD from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

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