Description

Book Synopsis
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.

Table of Contents
  • 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

Artificial Intelligence

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    £164.19

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    A Paperback / softback by George Luger

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      View other formats and editions of Artificial Intelligence by George Luger

      Publisher: Pearson Education (US)
      Publication Date: 13/05/2008
      ISBN13: 9780321545893, 978-0321545893
      ISBN10: 0321545893
      Also in:
      Artificial intelligence (AI)

      Description

      Book Synopsis
      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.

      Table of Contents
      • 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

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