Artificial intelligence: a modern approach
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Harlow, United Kingdom
Pearson
[2022]
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| Edition: | Fourth edition, global edition |
| Series: | AI, Pearson series in artificial intelligence
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Authorized adaptation from the United States edition ... 4th edition ... published © 2021 |
| Physical Description: | 1166 Seiten Illustrationen, Diagramme |
| ISBN: | 9781292401133 1292401133 |
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| 245 | 1 | 0 | |a Artificial intelligence |b a modern approach |c Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| 250 | |a Fourth edition, global edition | ||
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Record in the Search Index
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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. 19 19 23 35 45 49 52 53 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. 54 54 57 60 65 78 78 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. 81 81 84 89 94 102 115 122 124 4 Search in Complex Environments 4.1 Local Search and Optimization Problems. 4.2 Local Search in Continuous Spaces. 4.3 Search with Nondeterministic Actions . 4.4 Search in Partially Observable Environments. 4.5 Online Search Agents and Unknown Environments . Summary. Bibliographical and Historical Notes. 128 128 137 140
144 152 159 160 5 Constraint Satisfaction Problems 5.1 Defining Constraint Satisfaction Problems. 5.2 Constraint Propagation: Inference in CSPs. 164 164 169 11
Contents 6 III 5.3 Backtracking Search for CSPs. 5.4 Local Search for CSPs. 5.5 The Structure of Problems. Summary. Bibliographical and Historical Notes. 175 181 183 187 188 Adversarial Search and Games 6.1 Game Theory . 6.2 Optimal Decisions in Games . 6.3 Heuristic Alpha-Beta Tree Search . 6.4 Monte Carlo Tree Search. 6.5 Stochastic Games. 6.6 Partially Observable Games. . . 6.7 Limitations of Game Search Algorithms. Summary. Bibliographical and Historical Notes. 192 192 194 202 207 210 214
219 220 221 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. 226 227 228 232 235 240 250 255 264 265 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. 269 269 274 283 289 295 296 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 . 298 298 300 304 311 316 327 328
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. 332 332 335 340 344 347 351 355 356 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. 362 362 366 371 374 383 392 396 397 398 IV Uncertain knowledge and reasoning 12 Quantifying Uncertainty 12.1 Acting under Uncertainty. 12.2 Basic Probability Notation. 12.3 Inference Using Full loint 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. 403 403 406 413 415 417 420 422 425 426 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. 430 430 432 445 453 467 471 472 14 Probabilistic Reasoning over Time 14.1 Time and Uncertainty. 14.2 Inference in Temporal Models. 479 479 483 13
Contents 14.3 Hidden Markov Models. 14.4 Kalman Filters. 14.5 Dynamic Bayesian Networks. Summary. Bibliographical and Historical Notes. 491 497 503 514 515 15 Making Simple Decisions 15.1 Combining Beliefs and Desires under Uncertainty. 15.2 The Basis of Utility Theory. 15.3 Utility Functions. 15.4 Multiattribute Utility Functions. 15.5 Decision Networks. 15.6 The Value of Information. 15.7 Unknown Preferences. Summary. Bibliographical and Historical Notes. 518 518 519 522 530 534 537 543 547
547 16 Making Complex Decisions 16.1 Sequential Decision Problems. 16.2 Algorithms for MDPs. 16.3 Bandit Problems. 16.4 Partially Observable MDPs. 16.5 Algorithms for Solving POMDPs. Summary. Bibliographical and Historical Notes. 552 552 562 571 578 580 585 586 17 Multiagent Decision Making 17.1 Properties of Multiagent Environments. 17.2 Non-Cooperative Game Theory. 17.3 Cooperative Game Theory. 17.4 Making Collective Decisions. Summary. Bibliographical and Historical Notes. 589 589 595 616 622 635 636 18 Probabilistic Programming 18.1 Relational Probability
Models. 18.2 Open-Universe Probability Models. 18.3 Keeping Track of a Complex World. 18.4 Programs as Probability Models. Summary. Bibliographical and Historical Notes. 641 642 648 655 660 664 665 V Machine Learning 19 Learning from Examples 19.1 Forms of Learning. 669 669
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 HistoricalNotes. 671 675 683 690 694 704 714 722 732 733 20 Knowledge in Learning 20.1 A Logical Formulation of Learning. 20.2 Knowledge in Learning. 20.3 Explanation-Based Learning . 20.4 Learning Using Relevance Information. 20.5 Inductive
Logic Programming. Summary. Bibliographical and HistoricalNotes. 739 739 747 750 754 758 767 768 21 Learning Probabilistic Models 21.1 Statistical Learning . 21.2 Learning with Complete Data. 21.3 Learning with Hidden Variables: The EM Algorithm. Summary. Bibliographical and HistoricalNotes . . . . 772 772 775 788 797 798 22 Deep Learning 22.1 Simple Feedforward Networks . 22.2 Computation Graphs for Deep Learning . 22.3 Convolutional Networks. 22.4 Learning Algorithms. 22.5 Generalization. 22.6 Recurrent Neural Networks. 22.7 Unsupervised
Learning and Transfer Learning. 22.8 Applications. Summary. Bibliographical and Historical Notes. 801 802 807 811 816 819 823 826 833 835 836 23 Reinforcement Learning 23.1 Learning from Rewards. 23.2 Passive Reinforcement Learning . 23.3 Active Reinforcement Learning. 23.4 Generalization in Reinforcement Learning. 23.5 Policy Search . 23.6 Apprenticeship and Inverse Reinforcement Learning. 840 840 842 848 854 861 863 15
Contents 23.7 Applications of Reinforcement Learning. Summary. Bibliographical and Historical Notes. VI 866 869 870 Communicating, perceiving, and acting 24 Natural Language Processing 24.1 Language Models. 24.2 Grammar. 24.3 Parsing. 24.4 Augmented Grammars. 24.5 Complications of Real Natural Language. 24.6 Natural Language Tasks. Summary. Bibliographical and Historical Notes. 874 874 884 886 892 896 900 901 902 25 Deep Learning for Natural Language Processing 25.1 Word Embeddings. 25.2 Recurrent Neural Networks for
NLP. 25.3 Sequence-to֊Sequence Models. 25.4 The Transformer Architecture. 25.5 Pretraining and Transfer Learning. 25.6 State of the art. Summary. Bibliographical and Historical Notes. 907 907 911 915 919 922 926 929 929 26 Robotics 26.1 Robots. 26.2 Robot Hardware. 26.3 What kind of problem is robotics solving?. 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. 932 932 933 937 938 945 963 965 968 975 978 981 982 27 Computer Vision 988 27.1 Introduction. 988 27.2 Image Formation. 989 27.3 Simple Image Features . 995 27.4 Classifying Images.1002 27.5 Detecting Objects.1006
Contents 27.6 The 3D World.1008 27.7 Using Computer Vision. 1013 Summary.1026 Bibliographical and Historical Notes. 1027 VII Conclusions 28 Philosophy, Ethics, and Safety of AI 1032 28.1 The Limits of AI.1032 28.2 Can Machines Really Think?. 1035 28.3 The Ethics of AI.1037 Summary. 1056 Bibliographical and Historical Notes. 1057 29 The Future of AI 1063 29.1 AI Components.1063 29.2 AI Architectures.1069 A Mathematical Background 1074 A.l Complexity Analysis and 0() Notation. 1074 A.2 Vectors, Matrices, and Linear Algebra
. 1076 A.3 Probability Distributions. 1078 Bibliographical and Historical Notes.1080 В Notes on Languages and Algorithms 1081 B.l Defining Languages with Backus֊Naur Form (BNF). 1081 B.2 Describing Algorithms with Pseudocode.1082 B.3 Online Supplemental Material.1083 Bibliography 1084 Index 1119 |
| adam_txt |
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. 19 19 23 35 45 49 52 53 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. 54 54 57 60 65 78 78 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. 81 81 84 89 94 102 115 122 124 4 Search in Complex Environments 4.1 Local Search and Optimization Problems. 4.2 Local Search in Continuous Spaces. 4.3 Search with Nondeterministic Actions . 4.4 Search in Partially Observable Environments. 4.5 Online Search Agents and Unknown Environments . Summary. Bibliographical and Historical Notes. 128 128 137 140
144 152 159 160 5 Constraint Satisfaction Problems 5.1 Defining Constraint Satisfaction Problems. 5.2 Constraint Propagation: Inference in CSPs. 164 164 169 11
Contents 6 III 5.3 Backtracking Search for CSPs. 5.4 Local Search for CSPs. 5.5 The Structure of Problems. Summary. Bibliographical and Historical Notes. 175 181 183 187 188 Adversarial Search and Games 6.1 Game Theory . 6.2 Optimal Decisions in Games . 6.3 Heuristic Alpha-Beta Tree Search . 6.4 Monte Carlo Tree Search. 6.5 Stochastic Games. 6.6 Partially Observable Games. . . 6.7 Limitations of Game Search Algorithms. Summary. Bibliographical and Historical Notes. 192 192 194 202 207 210 214
219 220 221 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. 226 227 228 232 235 240 250 255 264 265 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. 269 269 274 283 289 295 296 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 . 298 298 300 304 311 316 327 328
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. 332 332 335 340 344 347 351 355 356 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. 362 362 366 371 374 383 392 396 397 398 IV Uncertain knowledge and reasoning 12 Quantifying Uncertainty 12.1 Acting under Uncertainty. 12.2 Basic Probability Notation. 12.3 Inference Using Full loint 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. 403 403 406 413 415 417 420 422 425 426 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. 430 430 432 445 453 467 471 472 14 Probabilistic Reasoning over Time 14.1 Time and Uncertainty. 14.2 Inference in Temporal Models. 479 479 483 13
Contents 14.3 Hidden Markov Models. 14.4 Kalman Filters. 14.5 Dynamic Bayesian Networks. Summary. Bibliographical and Historical Notes. 491 497 503 514 515 15 Making Simple Decisions 15.1 Combining Beliefs and Desires under Uncertainty. 15.2 The Basis of Utility Theory. 15.3 Utility Functions. 15.4 Multiattribute Utility Functions. 15.5 Decision Networks. 15.6 The Value of Information. 15.7 Unknown Preferences. Summary. Bibliographical and Historical Notes. 518 518 519 522 530 534 537 543 547
547 16 Making Complex Decisions 16.1 Sequential Decision Problems. 16.2 Algorithms for MDPs. 16.3 Bandit Problems. 16.4 Partially Observable MDPs. 16.5 Algorithms for Solving POMDPs. Summary. Bibliographical and Historical Notes. 552 552 562 571 578 580 585 586 17 Multiagent Decision Making 17.1 Properties of Multiagent Environments. 17.2 Non-Cooperative Game Theory. 17.3 Cooperative Game Theory. 17.4 Making Collective Decisions. Summary. Bibliographical and Historical Notes. 589 589 595 616 622 635 636 18 Probabilistic Programming 18.1 Relational Probability
Models. 18.2 Open-Universe Probability Models. 18.3 Keeping Track of a Complex World. 18.4 Programs as Probability Models. Summary. Bibliographical and Historical Notes. 641 642 648 655 660 664 665 V Machine Learning 19 Learning from Examples 19.1 Forms of Learning. 669 669
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 HistoricalNotes. 671 675 683 690 694 704 714 722 732 733 20 Knowledge in Learning 20.1 A Logical Formulation of Learning. 20.2 Knowledge in Learning. 20.3 Explanation-Based Learning . 20.4 Learning Using Relevance Information. 20.5 Inductive
Logic Programming. Summary. Bibliographical and HistoricalNotes. 739 739 747 750 754 758 767 768 21 Learning Probabilistic Models 21.1 Statistical Learning . 21.2 Learning with Complete Data. 21.3 Learning with Hidden Variables: The EM Algorithm. Summary. Bibliographical and HistoricalNotes . . . . 772 772 775 788 797 798 22 Deep Learning 22.1 Simple Feedforward Networks . 22.2 Computation Graphs for Deep Learning . 22.3 Convolutional Networks. 22.4 Learning Algorithms. 22.5 Generalization. 22.6 Recurrent Neural Networks. 22.7 Unsupervised
Learning and Transfer Learning. 22.8 Applications. Summary. Bibliographical and Historical Notes. 801 802 807 811 816 819 823 826 833 835 836 23 Reinforcement Learning 23.1 Learning from Rewards. 23.2 Passive Reinforcement Learning . 23.3 Active Reinforcement Learning. 23.4 Generalization in Reinforcement Learning. 23.5 Policy Search . 23.6 Apprenticeship and Inverse Reinforcement Learning. 840 840 842 848 854 861 863 15
Contents 23.7 Applications of Reinforcement Learning. Summary. Bibliographical and Historical Notes. VI 866 869 870 Communicating, perceiving, and acting 24 Natural Language Processing 24.1 Language Models. 24.2 Grammar. 24.3 Parsing. 24.4 Augmented Grammars. 24.5 Complications of Real Natural Language. 24.6 Natural Language Tasks. Summary. Bibliographical and Historical Notes. 874 874 884 886 892 896 900 901 902 25 Deep Learning for Natural Language Processing 25.1 Word Embeddings. 25.2 Recurrent Neural Networks for
NLP. 25.3 Sequence-to֊Sequence Models. 25.4 The Transformer Architecture. 25.5 Pretraining and Transfer Learning. 25.6 State of the art. Summary. Bibliographical and Historical Notes. 907 907 911 915 919 922 926 929 929 26 Robotics 26.1 Robots. 26.2 Robot Hardware. 26.3 What kind of problem is robotics solving?. 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. 932 932 933 937 938 945 963 965 968 975 978 981 982 27 Computer Vision 988 27.1 Introduction. 988 27.2 Image Formation. 989 27.3 Simple Image Features . 995 27.4 Classifying Images.1002 27.5 Detecting Objects.1006
Contents 27.6 The 3D World.1008 27.7 Using Computer Vision. 1013 Summary.1026 Bibliographical and Historical Notes. 1027 VII Conclusions 28 Philosophy, Ethics, and Safety of AI 1032 28.1 The Limits of AI.1032 28.2 Can Machines Really Think?. 1035 28.3 The Ethics of AI.1037 Summary. 1056 Bibliographical and Historical Notes. 1057 29 The Future of AI 1063 29.1 AI Components.1063 29.2 AI Architectures.1069 A Mathematical Background 1074 A.l Complexity Analysis and 0() Notation. 1074 A.2 Vectors, Matrices, and Linear Algebra
. 1076 A.3 Probability Distributions. 1078 Bibliographical and Historical Notes.1080 В Notes on Languages and Algorithms 1081 B.l Defining Languages with Backus֊Naur Form (BNF). 1081 B.2 Describing Algorithms with Pseudocode.1082 B.3 Online Supplemental Material.1083 Bibliography 1084 Index 1119 |
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| author | Russell, Stuart J. 1962- Norvig, Peter 1956- |
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| author_facet | Russell, Stuart J. 1962- Norvig, Peter 1956- |
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| author_sort | Russell, Stuart J. 1962- |
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| discipline | Informatik |
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| edition | Fourth edition, global edition |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV047376713 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:46:23Z |
| indexdate | 2025-02-24T09:00:58Z |
| institution | BVB |
| isbn | 9781292401133 1292401133 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032778401 |
| oclc_num | 1252696956 |
| open_access_boolean | |
| owner | DE-945 DE-739 DE-860 DE-573 DE-898 DE-BY-UBR DE-11 DE-523 DE-20 DE-N2 DE-384 DE-Aug4 DE-634 DE-473 DE-BY-UBG DE-703 DE-29 DE-1102 DE-1050 DE-861 DE-858 DE-2070s DE-355 DE-BY-UBR DE-1046 DE-29T DE-4325 |
| owner_facet | DE-945 DE-739 DE-860 DE-573 DE-898 DE-BY-UBR DE-11 DE-523 DE-20 DE-N2 DE-384 DE-Aug4 DE-634 DE-473 DE-BY-UBG DE-703 DE-29 DE-1102 DE-1050 DE-861 DE-858 DE-2070s DE-355 DE-BY-UBR DE-1046 DE-29T DE-4325 |
| physical | 1166 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Pearson |
| record_format | marc |
| series2 | AI, Pearson series in artificial intelligence |
| spelling | Russell, Stuart J. 1962- Verfasser (DE-588)13770741X aut Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] Fourth edition, global edition Harlow, United Kingdom Pearson [2022] © 2022 1166 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier AI, Pearson series in artificial intelligence Authorized adaptation from the United States edition ... 4th edition ... published © 2021 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Norvig, Peter 1956- Verfasser (DE-588)135811465 aut Äquivalent 4th United States edition 2021 978-0-13-461099-3 (DE-604)BV044647454 Erscheint auch als Online-Ausgabe 978-1-292-40117-1 (DE-604)BV047292074 Vorangegangen ist Third edition, global edition 2016 978-1-292-15396-4 (DE-604)BV043401101 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032778401&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Russell, Stuart J. 1962- Norvig, Peter 1956- Artificial intelligence a modern approach Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4123623-3 |
| title | Artificial intelligence a modern approach |
| title_auth | Artificial intelligence a modern approach |
| title_exact_search | Artificial intelligence a modern approach |
| title_exact_search_txtP | Artificial intelligence a modern approach |
| title_full | Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| title_fullStr | Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang [und 8 weitere] |
| title_full_unstemmed | Artificial intelligence a modern approach Stuart J. Russell and 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 |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Künstliche Intelligenz Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032778401&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT russellstuartj artificialintelligenceamodernapproach AT norvigpeter artificialintelligenceamodernapproach |