George Lugeris 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.
Summary
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 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 . References and citations are updated
throughout the Sixth Edition.
For all readers interested in artificial intelligence.
Table of Contents
PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1 1 AI: HISTORY AND APPLICATIONS 3 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice 3 1.2 Overview of AI Application Areas 20 1.3 Artificial Intelligence-A Summary 30 1.4 Epilogue and References 31 1.5 Exercises 33 PART II ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH 35 2 THE PREDICATE CALCULUS 45 2.0 Introduction 45 2.1 The Propositional Calculus 45 2.2 The Predicate Calculus 50 2.3 Using Inference Rules to Produce Predicate Calculus Expressions 62 2.4 Application: A Logic-Based Financial Advisor 73 2.5 Epilogue and References 77 2.6 Exercises 77 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 79 3.0 Introduction 79 3.1 Graph Theory 82 3.2 Strategies for State Space Search 93 3.3 Using the State Space to Represent Reasoning with the Predicate Calculus 107 3.4 Epilogue and References 121 3.5 Exercises 121 4 HEURISTIC SEARCH 123 4.0 Introduction 123 4.1 Hill Climbing and Dynamic Programming 127 4.2 The Best-First Search Algorithm 133 4.3 Admissibility, Monotonicity, and Informedness 145 4.4 Using Heuristics in Games 150 4.5 Complexity Issues 157 4.6 Epilogue and References 161 4.7 Exercises 162 5 STOCHASTIC METHODS 165 5.0 Introduction 165 5.1 The Elements of Counting 167 5.2 Elements of Probability Theory 170 5.3 Applications of the Stochastic Methodology 182 5.4 Bayes' Theorem 184 5.5 Epilogue and References 190 5.6 Exercises 191 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH 193 6.0 Introduction 193 6.1 Recursion-Based Search 194 6.2 Production Systems 200 6.3 The Blackboard Architecture for Problem Solving 187 6.4 Epilogue and References 219 6.5 Exercises 220 PART III CAPTURING INTELLIGENCE: THE AI CHALLENGE 223 7 KNOWLEDGE REPRESENTATION 227 7.0 Issues in Knowledge Representation 227 7.1 A Brief History of AI Representational Systems 228 7.2 Conceptual Graphs: A Network Language 248 7.3 Alternative Representations and Ontologies 258 7.4 Agent Based and Distributed Problem Solving 265 7.5 Epilogue and References 270 7.6 Exercises 273 8 STRONG METHOD PR
