Artificial intelligence : a modern approach

Author(s)

• Russell, Stuart J. (Stuart Jonathan)
• Norvig, Peter

Bibliographic Information

Artificial intelligence : a modern approach

Stuart Russell, Peter Norvig

（Pearson new international edition）

Pearson, c2014

3rd ed

• : [pbk.]

Note

"Pearson custom library"--Table of contents (p. i)

Includes bibliographical references (p. 1048-1078) and index

Description and Table of Contents

Description

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. View chapters 3 and 4 from the Third Edition. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle (TM), NOOK (TM), and the iPhone (R)/iPad (R). You can also purchase the eText for 180 days through CourseSmart http://www.mypearsonstore.com/bookstore/product.asp?isbn=0136067336

Table of Contents

I Artificial Intelligence 1 Introduction 1.1 What is AI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The Foundations of Artificial Intelligence . . . . . . . . . . . . . . . . . . 5 1.3 The History of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . 16 1.4 The State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 29 2 Intelligent Agents 2.1 Agents and Environments . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.2 Good Behavior: The Concept of Rationality . . . . . . . . . . . . . . . . 36 2.3 The Nature of Environments . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.4 The Structure of Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 59 II Problem-solving 3 Solving Problems by Searching 3.1 Problem-Solving Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.2 Example Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.3 Searching for Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.4 Uninformed Search Strategies . . . . . . . . . . . . . . . . . . . . . . . . 81 3.5 Informed (Heuristic) Search Strategies . . . . . . . . . . . . . . . . . . . 92 3.6 Heuristic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 3.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 108 4 Beyond Classical Search 4.1 Local Search Algorithms and Optimization Problems . . . . . . . . . . . 120 4.2 Local Search in Continuous Spaces . . . . . . . . . . . . . . . . . . . . . 129 4.3 Searching with Nondeterministic Actions . . . . . . . . . . . . . . . . . . 133 4.4 Searching with Partial Observations . . . . . . . . . . . . . . . . . . . . . 138 4.5 Online Search Agents and Unknown Environments . . . . . . . . . . . . 147 4.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 153 5 Adversarial Search 5.1 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 5.2 Optimal Decisions in Games . . . . . . . . . . . . . . . . . . . . . . . . 163 5.3 Alpha-Beta Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 5.4 Imperfect Real-Time Decisions . . . . . . . . . . . . . . . . . . . . . . . 171 5.5 Stochastic Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 5.6 Partially Observable Games . . . . . . . . . . . . . . . . . . . . . . . . . 180 5.7 State-of-the-Art Game Programs . . . . . . . . . . . . . . . . . . . . . . 185 5.8 Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 5.9 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 189 6 Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems . . . . . . . . . . . . . . . . . 202 6.2 Constraint Propagation: Inference in CSPs . . . . . . . . . . . . . . . . . 208 6.3 Backtracking Search for CSPs . . . . . . . . . . . . . . . . . . . . . . . . 214 6.4 Local Search for CSPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 6.5 The Structure of Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 222 6.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 227 III Knowledge, Reasoning, and Planning 7 Logical Agents 7.1 Knowledge-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . 235 7.2 The Wumpus World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 7.3 Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 7.4 Propositional Logic: A Very Simple Logic . . . . . . . . . . . . . . . . . 243 7.5 Propositional Theorem Proving . . . . . . . . . . . . . . . . . . . . . . . 249 7.6 Effective Propositional Model Checking . . . . . . . . . . . . . . . . . . 259 7.7 Agents Based on Propositional Logic . . . . . . . . . . . . . . . . . . . . 265 7.8 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 274 8 First-Order Logic 8.1 Representation Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . 285 8.2 Syntax and Semantics of First-Order Logic . . . . . . . . . . . . . . . . . 290 8.3 Using First-Order Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 8.4 Knowledge Engineering in First-Order Logic . . . . . . . . . . . . . . . . 307 8.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 313 9 Inference in First-Order Logic 9.1 Propositional vs. First-Order Inference . . . . . . . . . . . . . . . . . . . 322 9.2 Unification and Lifting . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 9.3 Forward Chaining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 9.4 Backward Chaining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 9.5 Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 9.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 357 10 Classical Planning 10.1 Definition of Classical Planning . . . . . . . . . . . . . . . . . . . . . . . 366 10.2 Algorithms for Planning as State-Space Search . . . . . . . . . . . . . . . 373 10.3 Planning Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 10.4 Other Classical Planning Approaches . . . . . . . . . . . . . . . . . . . . 387 10.5 Analysis of Planning Approaches . . . . . . . . . . . . . . . . . . . . . . 392 10.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 393 11 Planning and Acting in the Real World 11.1 Time, Schedules, and Resources . . . . . . . . . . . . . . . . . . . . . . . 401 11.2 Hierarchical Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406 11.3 Planning and Acting in Nondeterministic Domains . . . . . . . . . . . . . 415 11.4 Multiagent Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 11.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 430 12 Knowledge Representation 12.1 Ontological Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 12.2 Categories and Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 12.3 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 12.4 Mental Events and Mental Objects . . . . . . . . . . . . . . . . . . . . . 450 12.5 Reasoning Systems for Categories . . . . . . . . . . . . . . . . . . . . . 453 12.6 Reasoning with Default Information . . . . . . . . . . . . . . . . . . . . 458 12.7 The Internet Shopping World . . . . . . . . . . . . . . . . . . . . . . . . 462 12.8 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 467 IV Uncertain Knowledge and Reasoning 13 Quantifying Uncertainty 13.1 Acting under Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 480 13.2 Basic Probability Notation . . . . . . . . . . . . . . . . . . . . . . . . . . 483 13.3 Inference Using Full Joint Distributions . . . . . . . . . . . . . . . . . . . 490 13.4 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 13.5 Bayes' Rule and Its Use . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 13.6 The Wumpus World Revisited . . . . . . . . . . . . . . . . . . . . . . . . 499 13.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 503 14 Probabilistic Reasoning 14.1 Representing Knowledge in an Uncertain Domain . . . . . . . . . . . . . 510 14.2 The Semantics of Bayesian Networks . . . . . . . . . . . . . . . . . . . . 513 14.3 Efficient Representation of Conditional Distributions . . . . . . . . . . . . 518 14.4 Exact Inference in Bayesian Networks . . . . . . . . . . . . . . . . . . . 522 14.5 Approximate Inference in Bayesian Networks . . . . . . . . . . . . . . . 530 14.6 Relational and First-Order Probability Models . . . . . . . . . . . . . . . 539 14.7 Other Approaches to Uncertain Reasoning . . . . . . . . . . . . . . . . . 546 14.8 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 551 15 Probabilistic Reasoning over Time 15.1 Time and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 15.2 Inference in Temporal Models . . . . . . . . . . . . . . . . . . . . . . . . 570 15.3 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 15.4 Kalman Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 15.5 Dynamic Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . 590 15.6 Keeping Track of Many Objects . . . . . . . . . . . . . . . . . . . . . . . 599 15.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 603 16 Making Simple Decisions 16.1 Combining Beliefs and Desires under Uncertainty . . . . . . . . . . . . . 610 16.2 The Basis of Utility Theory . . . . . . . . . . . . . . . . . . . . . . . . . 611 16.3 Utility Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 16.4 Multiattribute Utility Functions . . . . . . . . . . . . . . . . . . . . . . . 622 16.5 Decision Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 16.6 The Value of Information . . . . . . . . . . . . . . . . . . . . . . . . . . 628 16.7 Decision-Theoretic Expert Systems . . . . . . . . . . . . . . . . . . . . . 633 16.8 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 636 17 Making Complex Decisions 17.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . 645 17.2 Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 17.3 Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 17.4 Partially Observable MDPs . . . . . . . . . . . . . . . . . . . . . . . . . 658 17.5 Decisions with Multiple Agents: Game Theory . . . . . . . . . . . . . . . 666 17.6 Mechanism Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679 17.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 684 V Learning 18 Learning from Examples 18.1 Forms of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693 18.2 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 18.3 Learning Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 18.4 Evaluating and Choosing the Best Hypothesis . . . . . . . . . . . . . . . 708 18.5 The Theory of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 18.6 Regression and Classification with Linear Models . . . . . . . . . . . . . 717 18.7 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 727 18.8 Nonparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 737 18.9 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . 744 18.10 Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 18.11 Practical Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 753 18.12 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 757 19 Knowledge in Learning 19.1 A Logical Formulation of Learning . . . . . . . . . . . . . . . . . . . . . 768 19.2 Knowledge in Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 19.3 Explanation-Based Learning . . . . . . . . . . . . . . . . . . . . . . . . 780 19.4 Learning Using Relevance Information . . . . . . . . . . . . . . . . . . . 784 19.5 Inductive Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . 788 19.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 797 20 Learning Probabilistic Models 20.1 Statistical Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 20.2 Learning with Complete Data . . . . . . . . . . . . . . . . . . . . . . . . 806 20.3 Learning with Hidden Variables: The EM Algorithm . . . . . . . . . . . . 816 20.4 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 825 21 Reinforcement Learning 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830 21.2 Passive Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 832 21.3 Active Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . 839 21.4 Generalization in Reinforcement Learning . . . . . . . . . . . . . . . . . 845 21.5 Policy Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848 21.6 Applications of Reinforcement Learning . . . . . . . . . . . . . . . . . . 850 21.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 853 VI Communicating, Perceiving, and Acting 22 Natural Language Processing 22.1 Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860 22.2 Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865 22.3 Information Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 22.4 Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873 22.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 882 23 Natural Language for Communication 23.1 Phrase Structure Grammars . . . . . . . . . . . . . . . . . . . . . . . . . 888 23.2 Syntactic Analysis (Parsing) . . . . . . . . . . . . . . . . . . . . . . . . . 892 23.3 Augmented Grammars and Semantic Interpretation . . . . . . . . . . . . 897 23.4 Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907 23.5 Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 912 23.6 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 918 24 Perception 24.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929 24.2 Early Image-Processing Operations . . . . . . . . . . . . . . . . . . . . . 935 24.3 Object Recognition by Appearance . . . . . . . . . . . . . . . . . . . . . 942 24.4 Reconstructing the 3D World . . . . . . . . . . . . . . . . . . . . . . . . 947 24.5 Object Recognition from Structural Information . . . . . . . . . . . . . . 957 24.6 Using Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961 24.7 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 965 25 Robotics 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971 25.2 Robot Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973 25.3 Robotic Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 25.4 Planning to Move . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986 25.5 Planning Uncertain Movements . . . . . . . . . . . . . . . . . . . . . . . 993 25.6 Moving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997 25.7 Robotic Software Architectures . . . . . . . . . . . . . . . . . . . . . . . 1003 25.8 Application Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 25.9 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 1010 VII Conclusions Bibliography 1063 Index 1095

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Details

• NCID: BB19248986
• ISBN:
  • 9781292024202
• Country Code: uk
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Harlow, Essex
• Pages/Volumes: ii, 1091 p.
• Size: 28 cm
• Classification:
  • DC23 : 006.3
• Subject Headings:
  • LCSH : Artificial intelligence
• Parent Bibliography ID:
  • BB14406048