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
  

About the Author

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.