Artificial intelligence: a modern approach
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken
Pearson
[2021]
|
| Ausgabe: | Fourth edition, [United States edition] |
| Schriftenreihe: | Pearson series in artificial intelligence
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 1166 Seiten Illustrationen, Diagramme |
| ISBN: | 9780134610993 |
Internformat
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Datensatz im Suchindex
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Contents I Artificial Intelligence 1 Introduction 1.1 What Is AI?. 1.2 The Foundations of Artificial Intelligence. 1.3 The History of Artificial Intelligence. 1.4 The State of the Art. 1.5 Risks and Benefits of AI. Summary. Bibliographical and Historical Notes. 1 1 5 17 27 31 34 35 2 Intelligent Agents 2.1 Agents and Environments. 2.2 Good Behavior: The Concept of Rationality. 2.3 The Nature of Environments. 2.4 The Structure of Agents. Summary. Bibliographical and Historical Notes. 36 36 39 42 47 60 60 II Problem-solving 3 Solving Problems by Searching 3.1 Problem-Solving
Agents. 3.2 Example Problems. 3.3 Search Algorithms. 3.4 Uninformed Search Strategies. 3.5 Informed (Heuristic) Search Strategies. 3.6 Heuristic Functions . Summary. Bibliographical and Historical Notes. 63 63 66 71 76 84 97 104 106 4 Search in Complex Environments 4.1 Local Search and Optimization Problems. 4.2 Local Search in Continuous Spaces. 4.3 Search with NondeterministicActions . 4.4 Search in Partially Observable Environments. 4.5 Online Search Agents and Unknown Environments . Summary. Bibliographical and Historical
Notes. 110 110 119 122 126 134 141 142 5 Adversarial Search and Games 5.1 Game Theory . 5.2 Optimal Decisions in Games . 146 146 148 xi
xii Contents 6 III 5.3 Heuristic Alpha-Beta Tree Search . 5.4 Monte Carlo Tree Search. 5.5 Stochastic Games. 5.6 Partially Observable Games . 5.7 Limitations of Game Search Algorithms. Summary. Bibliographical and Historical Notes . 156 161 164 168 173 174 175 Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems. 6.2 Constraint Propagation: Inference in CSPs. 6.3 Backtracking Search for CSPs. 6.4 Local Search for CSPs . 6.5 The Structure of Problems . . . Summary. Bibliographical and Historical Notes. 180 180 185 191 197 199 203 204 Knowledge, reasoning, and planning 7 Logical Agents 7.1 Knowledge-Based
Agents. 7.2 The Wumpus World. . 7.3 Logic. 7.4 Propositional Logic: A Very Simple Logic. 7.5 Propositional Theorem Proving . 7.6 Effective Propositional Model Checking. . 7.7 Agents Based on Propositional Logic. Summary. Bibliographical and Historical Notes. 208 209 210 214 217 222 232 237 246 247 8 First-Order Logic 8.1 Representation Revisited . 8.2 Syntax and Semantics of First-Order Logic. 8.3 Using First-Order Logic. 8.4 Knowledge Engineering in First-Order Logic. Summary. Bibliographical and Historical Notes. 251 251 256 265 271 277 278 9 Inference in First-Order Logic 9.1 Propositional vs. First-
Order Inference. 9.2 Unification and First-Order Inference. 9.3 Forward Chaining. 9.4 Backward Chaining. 9.5 Resolution. Summary. Bibliographical and Historical Notes . 280 280 282 286 293 298 309 310
Contents 10 Knowledge Representation 10.1 Ontological Engineering. 10.2 Categories and Objects . 10.3 Events. 10.4 Mental Objects and Modal Logic. 10.5 Reasoning Systems for Categories . 10.6 Reasoning with Default Information . Summary. Bibliographical and Historical Notes. 11 Automated Planning 11.1 Definition of Classical Planning. 11.2 Algorithms for Classical Planning. 11.3 Heuristics for Planning . 11.4 Hierarchical Planning. 11.5 Planning and Acting in Nondeterministic Domains. 11.6 Time, Schedules, and Resources. 11.7 Analysis of Planning
Approaches. Summary. Bibliographical and Historical Notes. IV 314 314 317 322 326 329 333 337 338 344 344 348 353 356 365 374 378 379 380 Uncertain knowledge and reasoning 12 Quantifying Uncertainty 12.1 Acting under Uncertainty. 12.2 Basic Probability Notation. 12.3 Inference Using Full Joint Distributions. 12.4 Independence . 12.5 Bayes’Rule and Its Use. 12.6 Naive Bayes Models. 12.7 The Wumpus World Revisited. Summary. Bibliographical and Historical Notes. 13 Probabilistic Reasoning 13.1 Representing Knowledge in an Uncertain Domain. 13.2 The Semantics of Bayesian
Networks. 13.3 Exact Inference in Bayesian Networks. 13.4 Approximate Inference for Bayesian Networks. 13.5 Causal Networks. Summary . Bibliographical and Historical Notes. 14 Probabilistic Reasoning over Time 14.1 14.2 Time and Uncertainty. Inference in Temporal Models. 385 385 388 395 397 399 402 404 407 408 412 412 414 427 435 449 453 454 461 461 465 xiii
xiv Contents 14.3 Hidden Markov Models. 14.4 Kalman Filters. 14.5 Dynamic Bayesian Networks. Summary. Bibliographical and Historical Notes. 473 479 485 496 497 15 Probabilistic Programming 15.1 Relational Probability Models. 15.2 Open-Universe Probability Models. 15.3 Keeping Track of a Complex World. 15.4 Programs as Probability Models. Summary. Bibliographical and Historical Notes. 500 501 507 514 519 523 524 16 Making Simple Decisions 16.1 Combining Beliefs and Desires under Uncertainty . . . 16.2 The Basis of Utility Theory. 16.3 Utility Functions. 16.4 Multiattribute Utility Functions. 16.5
Decision Networks. 16.6 The Value of Information. 16.7 Unknown Preferences. Summary. Bibliographical and Historical Notes . 528 528 529 532 540 544 547 553 557 557 17 Making Complex Decisions 17.1 Sequential Decision Problems. . 17.2 Algorithms for MDPs. 17.3 Bandit Problems. 17.4 Partially Observable MDPs. 17.5 Algorithms for Solving POMDPs. Summary. . Bibliographical and Historical Notes. 562 562 572 581 588 590 595 596 18 Multiagent Decision Making 18.1 Properties of Multiagent Environments . 18.2 Non-Cooperative Game Theory. 18.3 Cooperative Game
Theory. 18.4 Making Collective Decisions. Summary. Bibliographical and Historical Notes. 599 599 605 626 632 645 646 V Machine Learning 19 Learning from Examples 19.1 Forms of Learning. 651 651
Contents 19.2 Supervised Learning. 19.3 Learning Decision Trees. 19.4 Model Selection and Optimization . 19.5 The Theory of Learning. 19.6 Linear Regression and Classification. 19.7 Nonparametric Models . 19.8 Ensemble Learning. 19.9 Developing Machine Learning Systems. Summary. Bibliographical and Historical Notes. 653 657 665 672 676 686 696 704 714 715 20 Learning Probabilistic Models 20.1 Statistical Learning . 20.2 Learning with Complete Data. 20.3 Learning with Hidden Variables: The EM Algorithm . Summary. Bibliographical and
Historical Notes. 721 721 724 737 746 747 21 Deep Learning 21.1 Simple Feedforward Networks . 21.2 Computation Graphs for Deep Learning . 21.3 Convolutional Networks. 21.4 Learning Algorithms. 21.5 Generalization. 21.6 Recurrent Neural Networks. . 21.7 Unsupervised Learning and Transfer Learning. 21.8 Applications. Summary. Bibliographical and Historical Notes. 750 751 756 760 765 768 772 775 782 784 785 22 Reinforcement Learning 22.1 Learning from Rewards. 22.2 Passive Reinforcement Learning . 22.3 Active Reinforcement Learning. 22.4 Generalization in Reinforcement
Learning. . 22.5 Policy Search . 22.6 Apprenticeship and Inverse Reinforcement Learning. 22.7 Applications of Reinforcement Learning. Summary . Bibliographical and Historical Notes. 789 789 791 797 803 810 812 815 818 819 VI Communicating, perceiving, and acting 23 Natural Language Processing 23.1 Language Models. 23.2 Grammar. 823 823 833 XV
xvi Contents 23.3 Parsing. 23.4 Augmented Grammars. 23.5 Complications of Real Natural Language. 23.6 Natural Language Tasks . Summary. Bibliographical and Historical Notes. 835 841 845 849 850 851 24 Deep Learning for Natural Language Processing 24.1 Word Embeddings . . . . 24.2 Recurrent Neural Networks for NLP. 24.3 Sequence-to-Sequence Models. 24.4 The Transformer Architecture. 24.5 Pretraining and Transfer Learning. 24.6 State of the art. Summary. Bibliographical and Historical Notes . 856 856 860 864 868 871 875 878 878 25 Computer Vision 25.1 Introduction
. 25.2 Image Formation. 25.3 Simple Image Features . 25.4 Classifying Images. 25.5 Detecting Objects. 25.6 The 3D World. 25.7 Using Computer Vision. Summary. Bibliographical and Historical Notes. . 881 881 882 888 895 899 901 906 919 920 26 Robotics 26.1 Robots. 26.2 Robot Hardware. 26.3 What kind of problem is roboticssolving?. 26.4 Robotic Perception. 26.5 Planning and Control . 26.6 Planning Uncertain Movements. 26.7 Reinforcement Learning in Robotics . . . . . 26.8 Humans and
Robots. 26.9 Alternative Robotic Frameworks. 26.10 Application Domains. Summary. Bibliographical and Historical Notes. 925 925 926 930 931 938 956 958 961 968 971 974 975 VII Conclusions 27 Philosophy, Ethics, and Safety of AI 27.1 The Limits of AI. 981 981
Contents 27.2 Can Machines Really Think?. 984 27.3 The Ethics of AI. 986 Summary . .1005 Bibliographical and Historical Notes. 1006 28 The Future of AI 1012 28.1 AI Components.1012 28.2 AI Architectures.1018 A Mathematical Background 1023 A.l Complexity Analysis and 0() Notation.1023 A.2 Vectors, Matrices, and Linear Algebra .1025 A.3 Probability Distributions.1027 Bibliographical and Historical Notes. 1029 В Notes on Languages and Algorithms 1030 B.l Defining Languages with Backus-Naur Form (BNF). 1030 B.2 Describing Algorithms with Pseudocode.1031 B.3 Online Supplemental Material. 1032 Bibliography 1033 Index 1069
xvii |
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| title | Artificial intelligence a modern approach |
| title_auth | Artificial intelligence a modern approach |
| title_exact_search | Artificial intelligence a modern approach |
| title_full | Artificial intelligence a modern approach Stuart J. Russell ; Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| title_fullStr | Artificial intelligence a modern approach Stuart J. Russell ; Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| title_full_unstemmed | Artificial intelligence a modern approach Stuart J. Russell ; Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| title_short | Artificial intelligence |
| title_sort | artificial intelligence a modern approach |
| title_sub | a modern approach |
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| topic_facet | Künstliche Intelligenz Aufgabensammlung Lehrbuch |
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Inhaltsverzeichnis
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