Artificial intelligence: foundations of computational agents
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| Previous Title: | Poole, David L., 1958- Artificial intelligence |
|---|---|
| Main Authors: | , |
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore
Cambridge University Press
2023
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| Edition: | Third edition |
| Subjects: | |
| Online Access: | Volltext Inhaltsverzeichnis |
| Physical Description: | xxv, 870 Seiten Illustrationen, Diagramme |
| ISBN: | 9781009258197 |
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Contents Preface xxi I Agents in the World 1 1 Artificial Intelligence and Agents 1.1 What is Artificial Intelligence? . 1.1.1 Artificial and Natural Intelligence. 1.1.2 Natural Intelligence . 1.2 A Brief History of Artificial Intelligence. 1.2.1 Relationship to Other Disciplines. 1.3 Agents Situated in Environments. 1.4 Prototypical Applications. 1.4.1 An Autonomous Delivery and Helping Robot. 1.4.2 A Diagnostic Assistant. 1.4.3 A Tutoring Agent. 1.4.4 A Trading Agent. 1.4.5 Smart Home. 1.5 Agent Design Space. 1.5.1 Modularity . 1.5.2 Planning Horizon. 1.5.3 Representation. 1.5.4 Computational Limits. 1.5.5 Learning. 1.5.6
Uncertainty. 1.5.7 Preference. ix 3 3 5 8 9 12 14 16 16 17 19 20 20 21 22 23 24 26 28 29 31
Contents X 1.5.8 Number of Agents. 1.5.9 Interactivity. 1.5.10 Interaction of the Dimensions. 1.6 Designing Agents. 1.6.1 Simplifying Environments andSimplifying Agents . 1.6.2 Tasks. 1.6.3 Defining a Solution. 1.6.4 Representations. 1.7 Social Impact . 1.8 Overview of the Book . 1.9 Review. 1.10 References and Further Reading . 1.11 Exercises. 33 34 35 37 37 39 40 42 44 45 46 47 48 2 Agent Architectures and HierarchicalControl 2.1 Agents and Environments. 2.1.1 Controllers. 2.1.2 Belief States. 2.1.3 Agent Functions . 2.2 Hierarchical
Control. 2.3 Designing Agents. 2.3.1 Discrete, Continuous, and Hybrid. 2.3.2 Choosing Agent Functions . 2.3.3 Offline and Online Computation. 2.4 Social Impact . 2.5 Review. 2.6 References and Further Reading . 2.7 Exercises. 51 51 53 55 56 58 65 65 67 68 71 72 72 73 II Reasoning and Planning with Certainty 77 3 Searching for Solutions 3.1 Problem Solving as Search. 3.2 State Spaces. 3.3 Graph Searching . 3.3.1 Formalizing Graph Searching. 3.4 A Generic Searching Algorithm. 3.5 Uninformed Search Strategies. 3.5.1 Breadth-First Search. 3.5.2 Depth-First Search. 3.5.3 Iterative Deepening
. 3.5.4 Lowest-Cost-First Search . 3.6 Informed (Heuristic) Search. 3.6.1 A* Search. 79 79 81 84 84 86 88 90 91 96 99 101 102
xi Contents 3.7 3.8 3.9 3.10 3.11 3.12 3.6.2 Branch and Bound. 3.6.3 Designing a Heuristic Function. Pruning the Search Space . 3.7.1 Cycle Pruning. 3.7.2 Multiple-Path Pruning. 3.7.3 Summary of Search Strategies. Search Refinements. 3.8.1 Direction of Search. 3.8.2 Dynamic Programming. Social Impact . Review. References and Further Reading . Exercises. 105 108 109 109 110 113 114 115 117 120 121 121 122 4 Reasoning with Constraints 127 4.1 Variables and Constraints. 127 4.1.1 Variables and Assignments. 127 4.1.2 Constraints. 131 4.1.3 Constraint Satisfaction
Problems. 132 4.2 Solving CSPs by Searching. 133 4.3 Consistency Algorithms. 136 4.4 Domain Splitting. 141 4.5 Variable Elimination. 143 4.6 Local Search. 146 4.6.1 Iterative Best Improvement. 148 4.6.2 Randomized Algorithms. 149 4.6.3 Local Search Variants. 150 4.6.4 Evaluating Randomized Algorithms. 155 4.6.5 Random Restart. 157 4.7 Population-Based Methods. 158 4.8 Optimization . 161 4.8.1 Systematic Methods for Discrete Optimization. 164 4.8.2 Local Search for Optimization. 167 4.8.3 Gradient Descent for Continuous Functions . 167 4.9 Social Impact . 170 4.10
Review. 171 4.11 References and Further Reading . 171 4.12 Exercises. 172 5 Propositions and Inference 5.1 Propositions. 5.1.1 Syntax of the Propositional Calculus. 5.1.2 Semantics of the Propositional Calculus. 5.2 Propositional Constraints. 5.2.1 Clausal Form for CSPs. 177 177 177 178 181 182
Contents xii 5.2.2 Exploiting Propositional Structure in Local Search . Propositional Definite Clauses . 5.3.1 Queries and Answers . 5.3.2 Proofs . Querying the User. Knowledge-Level Debugging. 5.5.1 Incorrect Answers . 5.5.2 Missing Answers. Proving by Contradiction. 5.6.1 Horn Clauses. 5.6.2 Assumables and Conflicts. 5.6.3 Consistency-Based Diagnosis. 5.6.4 Reasoning with Assumptions and Horn Clauses. Complete Knowledge Assumption. 5.7.1 Non-Monotonic Reasoning. 5.7.2 Proof Procedures for Negation as Failure. Abduction. Causal Models. Social Impact .
Review. References and Further Reading . Exercises. 184 185 187 188 195 196 197 198 200 200 201 202 204 207 210 211 214 217 219 220 221 222 Deterministic Planning 6.1 Representing States, Actions, and Goals. 6.1.1 Explicit State-Space Representation . 6.1.2 The STRIPS Representation. 6.1.3 Feature-Based Representation of Actions. 6.1.4 Initial States and Goals. 6.2 Forward Planning. 6.3 Regression Planning. 6.4 Planning as a CSP. 6.4.1 Action Features. 6.5 Partial-Order Planning. 6.6 Social Impact . 6.7 Review. 6.8 References and FurtherReading . 6.9
Exercises. 231 232 233 235 237 238 239 241 244 247 248 252 253 253 254 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 6 III Learning and Reasoning with Uncertainty 7 Supervised Machine Learning 7.1 Learning Issues. 7.2 Supervised Learning Foundations. 259 261 262 266
Contents xiii 7.2.1 Evaluating Predictions. 7.2.2 Point Estimates with NoInput Features. 7.2.3 Types of Errors. 7.3 Basic Models for Supervised Learning. 7.3.1 Learning Decision Trees. 7.3.2 Linear Regression and Classification. 7.4 Overfitting. 7.4.1 Pseudocounts. 7.4.2 Regularization . 7.4.3 Cross Validation . 7.5 Composite Models. 7.5.1 Boosting. 7.5.2 Gradient-Boosted Trees. 7.6 Limitations. 7.7 Social Impact . 7.8 Review. 7.9 References and Further Reading. 7.10
Exercises. 269 276 278 281 281 288 297 301 302 304 307 309 311 315 317 319 319 320 8 Neural Networks and Deep Learning 8.1 Feedforward Neural Networks. 8.1.1 Parameter Learning . 8.2 Improved Optimization. 8.2.1 Momentum. 8.2.2 RMS-Prop. 8.2.3 Adam . 8.2.4 Initialization. 8.3 Improving Generalization. 8.4 Convolutional Neural Networks. 8.5 Neural Models for Sequences. 8.5.1 Word Embeddings. 8.5.2 Recurrent Neural Networks. 8.5.3 Long Short-Term Memory. 8.5.4 Attention and Transformers. 8.5.5 Large Language Models. 8.6 Other Neural Network Models. 8.6.1
Autoencoders. 8.6.2 Adversarial Networks. 8.6.3 Diffusion Models. 8.7 Social Impact . 8.8 Review. 8.9 References and Further Reading . 8.10 Exercises. 327 329 332 336 339 339 340 341 342 344 350 350 354 357 360 364 366 366 366 367 367 369 370 372
xiv Contents 9 Reasoning with Uncertainty 375 9.1 Probability. 375 9.1.1 Semantics of Probability. 377 9.1.2 Conditional Probability. 378 9.1.3 Expected Values. 383 9.2 Independence. 384 9.3 Belief Networks. 385 9.3.1 Observations and Queries. 387 9.3.2 Constructing Belief Networks. 388 9.3.3 Representing Conditional Probabilities and Factors . . . 394 9.4 Probabilistic Inference. 404 9.5 Exact Probabilistic Inference. 4Q5 9.5.1 Recursive Conditioning. 409 9.5.2 Variable Elimination for Belief Networks. 413 9.5.3 Exploiting Structure and Compilation. 416 9.6 Sequential Probability Models. 418 9.6.1 Markov Chains. 418 9.6.2 Hidden Markov Models. 420 9.6.3 Algorithms
for Monitoring andSmoothing. 426 9.6.4 Dynamic Belief Networks. 427 9.6.5 Time Granularity. 428 9.6.6 Probabilistic Language Models. 430 9.7 Stochastic Simulation. 436 9.7.1 Sampling from a Single Variable. 437 9.7.2 Forward Sampling. 439 9.7.3 Rejection Sampling. 440 9.7.4 Likelihood Weighting. 441 9.7.5 Importance Sampling. 443 9.7.6 Particle Filtering. 445 9.7.7 Markov Chain Monte Carlo. 447 9.8 Social Impact . 449 9.9 Review. 450 9.10 References and Further Reading. 450 9.11 Exercises. 451 10 Learning with Uncertainty 10.1 Probabilistic Learning. 10.2 Bayesian Learning
. 10.2.1 Learning Probabilities. 10.2.2 Probabilistic Classifiers . 10.2.3 Probabilistic Learning of Decision Trees. 10.2.4 Description Length. 10.3 Unsupervised Learning. 10.3.1. . 10.3.2 Expectation Maximization for Soft Clustering . 459 459 460 461 467 471 472 473 473 478
Contents XV 10.4 Learning Belief Networks. 10.4.1 Hidden Variables. 10.4.2 Missing Data. 10.4.3 Structure Learning. 10.4.4 General Case of Belief Network Learning. 10.5 Social Impact . 10.6 Review. 10.7 References and Further Reading. 10.8 Exercises. 481 482 482 484 485 486 486 487 487 11 Causality 11.1 Probabilistic Causal Models. 11.1.1 Do-notation. 11.1.2 D-separation . 11.2 Missing Data. 11.3 Inferring Causality. 11.3.1 Backdoor Criterion. 11.3.2 Do-calculus. 11.3.3 Front-Door
Criterion. 11.3.4 Simpson's Paradox. 11.4 Instrumental Variables. 11.5 Counterfactual Reasoning. 11.6 Social Impact . 11.7 Review. 11.8 References and Further Reading. 11.9 Exercises. 491 492 494 495 497 500 501 502 503 504 506 508 510 511 512 512 IV Planning and Acting with Uncertainty 515 12 Planning with Uncertainty 12.1 Preferences and Utility. 12.1.1 Axioms for Rationality. 12.1.2 Factored Utility. 12.1.3 Prospect Theory. 12.2 One-Off Decisions. 12.2.1 Single-Stage Decision Networks. 12.3 Sequential Decisions. 12.3.1 Decision
Networks. 12.3.2 Policies. 12.3.3 Optimizing Decision Networks using Search. 12.3.4 Variable Elimination for Decision Networks . 12.4 The Value of Information and Control. 12.5 Decision Processes. 12.5.1 Policies. 517 518 518 526 528 530 534 536 537 541 543 544 549 552 557
Contents xvi 12.6 12.7 12.8 12.9 12.5.2 Value Iteration . 12.5.3 Policy Iteration. 12.5.4 Dynamic Decision Networks. 12.5.5 Partially Observable Decision Processes. Social Impact . Review. References and Further Reading. Exercises. 560 563 565 569 571 572 573 573 13 Reinforcement Learning 583 13.1 Reinforcement Learning Problem. 583 13.2 Evolutionary Algorithms . 587 13.3 Temporal Differences. 588 13.4 Learning from Experiences . 588 13.4.1 . 589 13.5 Exploration and Exploitation. 591 13.6 Evaluating RL Algorithms. 594 13.7 On-Policy Learning. 595 13.8 Model-Based
RL. 597 13.9 RL with Generalization. 599 13.9.1 SARSA with Linear Function Approximation. 601 13.9.2 Escaping Local Optima . 603 13.10 Social Impact . 604 13.11 Review. 605 13.12 References and Further Reading. 605 13.13 Exercises. 606 14 Multiagent Systems 14.1 Multiagent Framework. 14.2 Representations of Games. 14.2.1 Normal-Form Games. 14.2.2 Extensive Form of a Game. 14.2.3 Multiagent Decision Networks. 14.3 Solving Perfect Information Games. 14.3.1 Adversarial Games. 14.4 Reasoning with Imperfect Information. 14.4.1 Computing Nash Equilibria. 14.5 Group Decision
Making. 14.6 Mechanism Design. 14.7 Multiagent Reinforcement Learning. 14.7.1 Perfect-Information Games. 14.7.2 Reinforcement Learning with Stochastic Policies. 14.7.3 State-of-the-Art Game Players . 14.8 Social Impact . 14.9 Review. 609 609 611 611 612 615 616 617 621 626 629 630 632 632 633 636 637 638
Contents 14.10 References and Further Reading. 14.11 Exercises. xvii 639 640 V Representing Individuals and Relations 643 15 Individuals and Relations 15.1 Exploiting Relational Structure. 15.2 Symbols and Semantics . 15.3 Predicate Calculus . 15.3.1 Semantics of Ground Logical Formulas. 15.3.2 Interpreting Variables. 15.4 Datalog: A Relational Rule Language. 15.4.1 Queries with Variables. 15.5 Proofs and Substitutions. 15.5.1 Instances and Substitutions . 15.5.2 Bottom-Up Procedure for Datalog. 15.5.3 Unification. 15.5.4 Definite Resolution with Variables. 15.6 Function Symbols and Data Structures. 15.6.1 Proof Procedures with Function Symbols. 15.7 Applications in Natural Language. 15.7.1 Using Definite Clauses for Context-Free
Grammars . . . 15.7.2 Augmenting the Grammar . 15.7.3 Building a Natural Language Interface to a Database . . 15.7.4 Comparison with Large Language Models. 15.8 Equality. 15.8.1 Allowing Equality Assertions. 15.8.2 Unique Names Assumption. 15.9 Complete Knowledge Assumption. 15.9.1 Complete Knowledge Assumption Proof Procedures . . 15.10 Social Impact . 15.11 Review. 15.12 References and Further Reading . 15.13 Exercises. 645 646 647 648 650 652 655 657 660 660 662 663 665 667 671 674 677 681 681 686 687 688 689 691 694 695 695 696 696 16 Knowledge Graphs and Ontologies 16.1 Knowledge Graphs. 16.1.1 Triples. 16.1.2 Individuals and Identifiers . 16.1.3 Graphical Representations . 16.2 Classes and
Properties. 16.2.1 Class and Property Hierarchies. 16.2.2 Designing Classes . 16.3 Ontologies and Knowledge Sharing. 701 701 701 705 706 707 708 711 714
Contents ■ xviii 16.3.1 Description Logic. 16.3.2 Top-Level Ontologies . 16.4 Social Impact . 718 I 723 I 726 I 16.5 16.6 16.7 Review. References and Further Reading . Exercises. 727 ■ 727 0 728 fl 17 Relational Learning and Probabilistic Reasoning 17.1 From Relations to Features and Random Variables. 17.2 Embedding-Based models. 17.2.1 Learning a Binary Relation. 17.2.2 Learning Knowledge Graphs. 17.3 Learning Interdependence of Relations . 17.3.1 Relational Probabilistic Models. 17.3.2 Collective Classification and Crowd Sourcing . 17.3.3 Language and Topic Models . 17.3.4 Some Specific Representations. 17.4 Existence and Identity Uncertainty. 17.5 Social Impact . 17.6
Review. 17.7 References and Further Reading . 17.8 Exercises. 731 | 732 ' 734 л 734 j 740 743 з 743 749 750 754 756 758 759 759 760 VI The Big Picture 18 The Social Impact of Artificial Intelligence 18.1 The Digital Economy. 18.2 Values and Bias. 18.3 Human-Centred Artificial Intelligence. 18.4 Work and Automation. 18.5 Transportation. 18.6 Sustainability. 18.7 Ethics. 18.8 Governance and Regulation. 18.9 Review. 18.10 Exercises. 765 767 768 769 770 773 774 775 778 782 783 784 19 Retrospect and Prospect 785 19.1 Deploying AI. 785 19.2 Agent Design Space
Revisited. 788 19.3 Looking Ahead. 792 19.4 References and Further Reading. 794 19.5 Exercises. 795
Contents xix Appendix A Mathematical Preliminariesand Notation A.l Rolling Average. A.2 Discrete Mathematics. A.3 Functions, Factors, and Arrays. A.4 Relations and the Relational Algebra. 797 797 798 799 800 Appendix В Mapping to Open-Source Packages B.l Gradient-Boosted Trees. B.2 Deep Learning. 803 803 804 References 807 Index of Algorithms 843 Index 847 |
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| genre | 1\p (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV049295097 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:37:56Z |
| indexdate | 2025-01-09T15:00:39Z |
| institution | BVB |
| isbn | 9781009258197 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034556438 |
| oclc_num | 1406338054 |
| open_access_boolean | 1 |
| owner | DE-521 DE-188 DE-83 DE-739 DE-2070s |
| owner_facet | DE-521 DE-188 DE-83 DE-739 DE-2070s |
| physical | xxv, 870 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Poole, David L. 1958- Verfasser (DE-588)141818018 aut Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Vancouver), Alan K. Mackworth (University of British Columbia, Vancouver) Third edition Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore Cambridge University Press 2023 xxv, 870 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Computational intelligence Textbooks Artificial intelligence Textbooks Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Wissensgraph (DE-588)1241153396 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Künstliche Intelligenz (DE-588)4033447-8 s Informatik (DE-588)4026894-9 s Maschinelles Lernen (DE-588)4193754-5 s Wissensgraph (DE-588)1241153396 s Ontologie Wissensverarbeitung (DE-588)4827894-4 s DE-188 Mackworth, Alan K. 1945- Verfasser (DE-588)141818115 aut Ersatz von Poole, David L., 1958- Artificial intelligence Second edition Cambridge, United Kingdom : Cambridge University Press, 2017 978-1-107-19539-4 (DE-604)BV044416064 http://artint.info/html/ArtInt.html kostenfrei Volltext 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=034556438&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Poole, David L. 1958- Mackworth, Alan K. 1945- Artificial intelligence foundations of computational agents Computational intelligence Textbooks Artificial intelligence Textbooks Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Wissensgraph (DE-588)1241153396 gnd Informatik (DE-588)4026894-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4827894-4 (DE-588)4033447-8 (DE-588)1241153396 (DE-588)4026894-9 (DE-588)4193754-5 (DE-588)4123623-3 |
| title | Artificial intelligence foundations of computational agents |
| title_auth | Artificial intelligence foundations of computational agents |
| title_exact_search | Artificial intelligence foundations of computational agents |
| title_exact_search_txtP | Artificial intelligence foundations of computational agents |
| title_full | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Vancouver), Alan K. Mackworth (University of British Columbia, Vancouver) |
| title_fullStr | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Vancouver), Alan K. Mackworth (University of British Columbia, Vancouver) |
| title_full_unstemmed | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Vancouver), Alan K. Mackworth (University of British Columbia, Vancouver) |
| title_old | Poole, David L., 1958- Artificial intelligence |
| title_short | Artificial intelligence |
| title_sort | artificial intelligence foundations of computational agents |
| title_sub | foundations of computational agents |
| topic | Computational intelligence Textbooks Artificial intelligence Textbooks Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Wissensgraph (DE-588)1241153396 gnd Informatik (DE-588)4026894-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computational intelligence Textbooks Artificial intelligence Textbooks Ontologie Wissensverarbeitung Künstliche Intelligenz Wissensgraph Informatik Maschinelles Lernen Lehrbuch |
| url | http://artint.info/html/ArtInt.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034556438&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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