BOOK
Artificial Intelligence: A Modern Approach, Global Edition
(2016)
Additional Information
Book Details
Abstract
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.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Artificial Intelligence A Modern Approach | iii | ||
| Copyright | iv | ||
| Dedication | v | ||
| Preface | vii | ||
| About the Authors | xii | ||
| Contents | xiii | ||
| 1. Introduction | 1 | ||
| 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 | 34 | ||
| 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 | ||
| 3. Solving Problems by Searching | 64 | ||
| 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 | 120 | ||
| 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 | 161 | ||
| 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 | 202 | ||
| 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 | ||
| 7. Logical Agents | 234 | ||
| 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 | 285 | ||
| 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 | 322 | ||
| 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 | 366 | ||
| 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 | 401 | ||
| 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 | 437 | ||
| 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 | ||
| 13. Quantifying Uncertainty | 480 | ||
| 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 | 510 | ||
| 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 | 566 | ||
| 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 | 610 | ||
| 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 | 645 | ||
| 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 | ||
| 18. Learning from Examples | 693 | ||
| 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 | 768 | ||
| 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 | 802 | ||
| 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 | 830 | ||
| 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 | ||
| 22. Natural Language Processing | 860 | ||
| 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 | 888 | ||
| 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 | 928 | ||
| 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 | 971 | ||
| 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 | ||
| 26. Philosophical Foundations | 1020 | ||
| 26.1 Weak AI: Can Machines Act Intelligently? | 1020 | ||
| 26.2 Strong AI: Can Machines Really Think? | 1026 | ||
| 26.3 The Ethics and Risks of Developing Artificial Intelligence | 1034 | ||
| 26.4 Summary, Bibliographical and Historical Notes, Exercises | 1040 | ||
| 27. AI: The Present and Future | 1044 | ||
| 27.1 Agent Components | 1044 | ||
| 27.2 Agent Architectures | 1047 | ||
| 27.3 Are We Going in the Right Direction? | 1049 | ||
| 27.4 What If AI Does Succeed? | 1051 | ||
| A. Mathematical background | 1053 | ||
| A.1 Complexity Analysis and O() Notation | 1053 | ||
| A.2 Vectors, Matrices, and Linear Algebra | 1055 | ||
| A.3 Probability Distributions | 1057 | ||
| B. Notes on Languages and Algorithms | 1060 | ||
| B.1 Defining Languages with Backus–Naur Form (BNF) | 1060 | ||
| B.2 Describing Algorithms with Pseudocode | 1061 | ||
| B.3 Online Help | 1062 | ||
| Bibliography | 1063 | ||
| Index | 1095 |