Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is "one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world." One of the other two courses, an introduction to database software, is being taught by Pearson authorDr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle(t), NOOK(t), and the iPhone(R)/iPad(R). To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full New York Timesarticle, click here.
| ISBN | 0132071487 | | Part volume | International Version | | ISBN13 | 9780132071482
(What's this?) | | Weight (grammes) | 1860 | | Publisher | Pearson Education (US) | | Published in | Upper Saddle River | | Imprint | Pearson | | Previous ISBN | 9780136042594 | | Format | Paperback | | Height (mm) | 254 | | Publication date | 01 Apr 2010 | | Width (mm) | 201 | | DEWEY | 006.3 | | Spine width (mm) | 51 | | DEWEY edition | DC22 | | Academic level | Tertiary education | | Pages | 1152 | |
|
|
|
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 Inferen
Enlarge
