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Artificial intelligence : structures and strategies for complex problem solving

Author: George F Luger
Publisher: Boston ; Mexico City : Pearson, 2009.
Edition/Format:   Print book : English : 6th edView all editions and formats
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Document Type: Book
All Authors / Contributors: George F Luger
ISBN: 9780321545893 0321545893 9780132090018 0132090015
OCLC Number: 427531583
Description: 754 p. : il
Contents: PART IARTIFICIAL INTELLIGENCE: ITS ROOTSAND SCOPE 11 AI: HISTORY AND APPLICATIONS 31.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, andHuman Artifice 31.2 Overview of AI Application Areas 201.3 Artificial Intelligence-A Summary 301.4 Epilogue and References 311.5 Exercises 33 PART IIARTIFICIAL INTELLIGENCE ASREPRESENTATION AND SEARCH 352 THE PREDICATE CALCULUS 452.0 Introduction 452.1 The Propositional Calculus 452.2 The Predicate Calculus 502.3 Using Inference Rules to Produce Predicate Calculus Expressions 622.4 Application: A Logic-Based Financial Advisor 732.5 Epilogue and References 772.6 Exercises 77 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 793.0 Introduction 793.1 Graph Theory 823.2 Strategies for State Space Search 933.3 Using the State Space to Represent Reasoning with the Predicate Calculus 1073.4 Epilogue and References 1213.5 Exercises 121 4 HEURISTIC SEARCH 1234.0 Introduction 1234.1 Hill Climbing and Dynamic Programming 1274.2 The Best-First Search Algorithm 1334.3 Admissibility, Monotonicity, and Informedness 1454.4 Using Heuristics in Games 1504.5 Complexity Issues 1574.6 Epilogue and References 1614.7 Exercises 162 5 STOCHASTIC METHODS 1655.0 Introduction 1655.1 The Elements of Counting 1675.2 Elements of Probability Theory 1705.3 Applications of the Stochastic Methodology 1825.4 Bayes' Theorem 1845.5 Epilogue and References 1905.6 Exercises 191 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH 1936.0 Introduction 1936.1 Recursion-Based Search 1946.2 Production Systems 2006.3 The Blackboard Architecture for Problem Solving 1876.4 Epilogue and References 2196.5 Exercises 220 PART IIICAPTURING INTELLIGENCE:THE AI CHALLENGE 2237 KNOWLEDGE REPRESENTATION 2277.0 Issues in Knowledge Representation 2277.1 A Brief History of AI Representational Systems 2287.2 Conceptual Graphs: A Network Language 2487.3 Alternative Representations and Ontologies 2587.4 Agent Based and Distributed Problem Solving 2657.5 Epilogue and References 2707.6 Exercises 273 8 STRONG METHOD PROBLEM SOLVING 2778.0 Introduction 2778.1 Overview of Expert System Technology 2798.2 Rule-Based Expert Systems 2868.3 Model-Based, Case Based, and Hybrid Systems 2988.4 Planning 3148.5 Epilogue and References 3298.6 Exercises 331 9 REASONING IN UNCERTAIN SITUATIONS 3339.0 Introduction 3339.1 Logic-Based Abductive Inference 3359.2 Abduction: Alternatives to Logic 3509.3 The Stochastic Approach to Uncertainty 3639.4 Epilogue and References 3789.5 Exercises 380 PART IVMACHINE LEARNING 38510 MACHINE LEARNING: SYMBOL-BASED 38710.0 Introduction 38710.1 A Framework for Symbol-based Learning 39010.2 Version Space Search 39610.3 The ID3 Decision Tree Induction Algorithm 40810.4 Inductive Bias and Learnability 41710.5 Knowledge and Learning 42210.6 Unsupervised Learning 43310.7 Reinforcement Learning 44210.8 Epilogue and References 44910.9 Exercises 450 11 MACHINE LEARNING: CONNECTIONIST 45311.0 Introduction 45311.1 Foundations for Connectionist Networks 45511.2 Perceptron Learning 45811.3 Backpropagation Learning 46711.4 Competitive Learning 47411.5 Hebbian Coincidence Learning 48411.6 Attractor Networks or "Memories" 49511.7 Epilogue and References 50511.8 Exercises 506 12 MACHINE LEARNING: GENETIC AND EMERGENT 50712.0 Genetic and Emergent Models of Learning 50712.1 The Genetic Algorithm 50912.2 Classifier Systems and Genetic Programming 51912.3 Artificial Life and Society-Based Learning 53012.4 Epilogue and References 54112.5 Exercises 542 13 MACHINE LEARNING: PROBABILISTIC 54313.0 Stochastic and Dynamic Models of Learning 54313.1 Hidden Markov Models (HMMs) 54413.2 Dynamic Bayesian Networks and Learning 55413.3 Stochastic Extensions to Reinforcement Learning 56413.4 Epilogue and References 56813.5 Exercises 570 PART VADVANCED TOPICS FOR AI PROBLEM SOLVING 57314 AUTOMATED REASONING 57514.0 Introduction to Weak Methods in Theorem Proving 57514.1 The General Problem Solver and Difference Tables 57614.2 Resolution Theorem Proving 58214.3 PROLOG and Automated Reasoning 60314.4 Further Issues in Automated Reasoning 60914.5 Epilogue and References 66614.6 Exercises 667 15 UNDERSTANDING NATURAL LANGUAGE 61915.0 The Natural Language Understanding Problem 61915.1 Deconstructing Language: An Analysis 62215.2 Syntax 62515.3 Transition Network Parsers and Semantics 63315.4 Stochastic Tools for Language Understanding 64915.5 Natural Language Applications 65815.6 Epilogue and References 63015.7 Exercises 632 PART VIEPILOGUE 67116 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 67316.0 Introduction 67316.1 Artificial Intelligence: A Revised Definition 67516.2 The Science of Intelligent Systems 68816.3 AI: Current Challanges and Future Direstions 69816.4 Epilogue and References 703Bibliography 705Author Index 735Subject Index 743
Responsibility: George F. Luger.

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