Artificial Intelligence: Structures and Strategies for Complex Problem Solving: International Edition

Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI. 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. Readers learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists.

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 PROBLEM SOLVING 277 8.0 Introduction 277 8.1 Overview of Expert System Technology 279 8.2 Rule-Based Expert Systems 286 8.3 Model-Based, Case Based, and Hybrid Systems 298 8.4 Planning 314 8.5 Epilogue and References 329 8.6 Exercises 331 9 REASONING IN UNCERTAIN SITUATIONS 333 9.0 Introduction 333 9.1 Logic-Based Abductive Inference 335 9.2 Abduction: Alternatives to Logic 350 9.3 The Stochastic Approach to Uncertainty 363 9.4 Epilogue and References 378 9.5 Exercises 380 PART IV: MACHINE LEARNING 385 10 MACHINE LEARNING: SYMBOL-BASED 387 10.0 Introduction 387 10.1 A Framework for Symbol-based Learning 390 10.2 Version Space Search 396 10.3 The ID3 Decision Tree Induction Algorithm 408 10.4 Inductive Bias and Learnability 417 10.5 Knowledge and Learning 422 10.6 Unsupervised Learning 433 10.7 Reinforcement Learning 442 10.8 Epilogue and References 449 10.9 Exercises 450 11 MACHINE LEARNING: CONNECTIONIST 453 11.0 Introduction 453 11.1 Foundations for Connectionist Networks 455 11.2 Perceptron Learning 458 11.3 Backpropagation Learning 467 11.4 Competitive Learning 474 11.5 Hebbian Coincidence Learning 484 11.6 Attractor Networks or “Memories” 495 11.7 Epilogue and References 505 11.8 Exercises 506 12 MACHINE LEARNING: GENETIC AND EMERGENT 507 12.0 Genetic and Emergent Models of Learning 507 12.1 The Genetic Algorithm 509 12.2 Classifier Systems and Genetic Programming 519 12.3 Artificial Life and Society-Based Learning 530 12.4 Epilogue and References 541 12.5 Exercises 542 13 MACHINE LEARNING: PROBABILISTIC 543 13.0 Stochastic and Dynamic Models of Learning 543 13.1 Hidden Markov Models (HMMs) 544 13.2 Dynamic Bayesian Networks and Learning 554 13.3 Stochastic Extensions to Reinforcement Learning 564 13.4 Epilogue and References 568 13.5 Exercises 570 PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING 573 14 AUTOMATED REASONING 575 14.0 Introduction to Weak Methods in Theorem Proving 575 14.1 The General Problem Solver and Difference Tables 576 14.2 Resolution Theorem Proving 582 14.3 PROLOG and Automated Reasoning 603 14.4 Further Issues in Automated Reasoning 609 14.5 Epilogue and References 666 14.6 Exercises 667 15 UNDERSTANDING NATURAL LANGUAGE 619 15.0 The Natural Language Understanding Problem 619 15.1 Deconstructing Language: An Analysis 622 15.2 Syntax 625 15.3 Transition Network Parsers and Semantics 633 15.4 Stochastic Tools for Language Understanding 649 15.5 Natural Language Applications 658 15.6 Epilogue and References 630 15.7 Exercises 632 PART VI: EPILOGUE 671 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 673 16.0 Introduction 673 16.1 Artificial Intelligence: A Revised Definition 675 16.2 The Science of Intelligent Systems 688 16.3 AI: Current Challanges and Future Direstions 698 16.4 Epilogue and References 703 Bibliography 705 Author Index 735 Subject Index 743

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.