Artificial intelligence: foundations of computational agents
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY ; Port Melbourne ; New Delhi ; Singapore
Cambridge University Press
2017
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xxviii, 792 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107195394 110719539X |
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| 245 | 1 | 0 | |a Artificial intelligence |b foundations of computational agents |c David L. Poole (University of British Columbia, Canada), Alan K. Mackworth (University of British Columbia, Canada) |
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| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY ; Port Melbourne ; New Delhi ; Singapore |b Cambridge University Press |c 2017 | |
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Record in the Search Index
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Contents
Contents v
Figures xv
I Agents in the World: What are Agents and How Can They be
Built? 1
1 Artificial Intelligence and Agents 3
1.1 What is Artificial Intelligence? . 3
1.1.1 Artificial and Natural Intelligence. 5
1.2 A Brief History of Artificial Intelligence. 7
1.2.1 Relationship to Other Disciplines. 10
1.3 Agents Situated in Environments. 11
1.4 Designing Agents. 13
1.4.1 Design Time, Offline and Online Computation. 13
1.4.2 Tasks. 15
1.4.3 Defining a Solution. 17
1.4.4 Representations. 19
1.5 Agent Design Space. 21
1.5.1 Modularity . 21
1.5.2 Planning Horizon. 23
1.5.3 Representation. 23
1.5.4 Computational Limits. 25
1.5.5 Learning. 27
v
VI
Contents
1.5.6 Uncertainty. 28
1.5.7 Preference. 29
1.5.8 Number of Agents. 30
1.5.9 Interaction. 30
1.5.10 Interaction of the Dimensions. 31
1.6 Prototypical Applications. 33
1.6.1 An Autonomous Delivery Robot. 34
1.6.2 A Diagnostic Assistant. 36
1.6.3 An Intelligent Tutoring System. 39
1.6.4 A Trading Agent. 41
1.6.5 Smart House . 43
1.7 Overview of the Book . 44
1.8 Review. 45
1.9 References and Further Reading . 46
1.10 Exercises. 47
2 Agent Architectures and Hierarchical Control 49
2.1 Agents. 50
2.2 Agent Systems. 51
2.2.1 The Agent Function . 51
2.3 Hierarchical Control. 56
2.4 Acting with Reasoning. 65
2.4.1 Agents Modeling the World. 65
2.4.2 Knowledge and Acting . 66
2.4.3 Design Time and Offline Computation. 67
2.4.4 Online Computation. 69
2.5 Review. 70
2.6 References and Further Reading . 71
2.7 Exercises. 71
II Reasoning, Planning and Learning with Certainty 75
3 Searching for Solutions 77
3.1 Problem Solving as Search. 77
3.2 State Spaces. 79
3.3 Graph Searching . 81
3.3.1 Formalizing Graph Searching. 82
3.4 A Generic Searching Algorithm. 85
3.5 Uninformed Search Strategies. 87
3.5.1 Breadth-First Search. 87
3.5.2 Depth-First Search. 90
3.5.3 Iterative Deepening . 94
Contents vii
3.5.4 Lowest-Cost-First Search . 97
3.6 Heuristic Search. 98
3.6.1 A*Search. 100
3.6.2 Designing a Heuristic Function. 104
3.7 Pruning the Search Space . 105
3.7.1 Cycle Pruning. 105
3.7.2 Multiple-Path Pruning. 106
3.7.3 Summary of Search Strategies. 109
3.8 More Sophisticated Search. 110
3.8.1 Branch and Bound. 110
3.8.2 Direction of Search. 113
3.8.3 Dynamic Programming. 115
3.9 Review. 119
3.10 References and Further Reading . 119
3.11 Exercises. 120
4 Reasoning with Constraints 125
4.1 Possible Worlds, Variables, and Constraints. 125
4.1.1 Variables and Worlds. 125
4.1.2 Constraints. 129
4.1.3 Constraint Satisfaction Problems. 131
4.2 Generate-and-Test Algorithms . 132
4.3 Solving CSPs Using Search . 133
4.4 Consistency Algorithms. 134
4.5 Domain Splitting. 139
4.6 Variable Elimination. 141
4.7 Local Search. 144
4.7.1 Iterative Best Improvement. 146
4.7.2 Randomized Algorithms. 148
4.7.3 Local Search Variants. 149
4.7.4 Evaluating Randomized Algorithms. 153
4.7.5 Random Restart. 156
4.8 Population-Based Methods. 156
4.9 Optimization . 160
4.9.1 Systematic Methods for Optimization. 162
4.9.2 Local Search for Optimization. 164
4.10 Review. 167
4.11 References and Further Reading . 167
4.12 Exercises. 168
5 Propositions and Inference 173
5.1 Propositions. 173
5.1.1 Syntax of Propositional Calculus. 174
Vlll
Contents
5.1.2 Semantics of the Propositional Calculus. 175
5.2 Propositional Constraints. 179
5.2.1 Clausal Form for Consistency Algorithms. 180
5.2.2 Exploiting Propositional Structure in Local Search . 181
5.3 Propositional Definite Clauses . 182
5.3.1 Questions and Answers. 185
5.3.2 Proofs . 186
5.4 Knowledge Representation Issues . 194
5.4.1 Background Knowledge and Observations. 194
5.4.2 Querying the User. 194
5.4.3 Knowledge-Level Explanation. 196
5.4.4 Knowledge-Level Debugging. 199
5.5 Proving by Contradiction. 204
5.5.1 Horn Clauses. 205
5.5.2 Assumables and Conflicts. 206
5.5.3 Consistency-Based Diagnosis. 207
5.5.4 Reasoning with Assumptions and Horn Clauses. 209
5.6 Complete Knowledge Assumption. 212
5.6.1 Non-monotonic Reasoning. 216
5.6.2 Proof Procedures for Negation as Failure. 217
5.7 Abduction. 220
5.8 Causal Models. 225
5.9 Review. 226
5.10 References and Further Reading. 227
5.11 Exercises. 228
6 Planning with Certainty 239
6.1 Representing States, Actions, and Goals. 240
6.1.1 Explicit State-Space Representation . 241
6.1.2 The STRIPS Representation. 243
6.1.3 Feature-Based Representation of Actions. 244
6.1.4 Initial States and Goals. 246
6.2 Forward Planning. 246
6.3 Regression Planning. 249
6.4 Planning as a CSP. 252
6.4.1 Action Features. 255
6.5 Partial-Order Planning. 257
6.6 Review. 260
6.7 References and Further Reading. 261
6.8 Exercises. 262
7 Supervised Machine Learning 267
7.1 Learning Issues. 268
Contents
IX
7.2 Supervised Learning. 271
7.2.1 Evaluating Predictions. 274
7.2.2 Types of Errors. 279
7.2.3 Point Estimates with No Input Features. 283
7.3 Basic Models for Supervised Learning. 285
7.3.1 Learning Decision Trees. 285
7.3.2 Linear Regression and Classification. 291
7.4 Overfitting. 298
7.4.1 Pseudocounts. 301
7.4.2 Regularization . 304
7.4.3 Cross Validation . 306
7.5 Neural Networks and Deep Learning. 308
7.6 Composite Models. 316
7.6.1 Random Forests. 317
7.6.2 Ensemble Learning. 318
7.7 Case-Based Reasoning. 320
7.8 Learning as Refining the Flypothesis Space. 323
7.8.1 Version-Space Learning. 325
7.8.2 Probably Approximately Correct Learning. 328
7.9 Review. 331
7.10 References and Further Reading. 331
7.11 Exercises. 333
III Reasoning, Learning and Acting with Uncertainty 341
8 Reasoning with Uncertainty 343
8.1 Probability. 343
8.1.1 Semantics of Probability. 345
8.1.2 Axioms for Probability. 347
8.1.3 Conditional Probability. 350
8.1.4 Expected Values. 355
8.1.5 Information. 356
8.2 Independence. 358
8.3 Belief Networks. 360
8.3.1 Observations and Queries. 362
8.3.2 Constructing Belief Networks. 363
8.4 Probabilistic Inference. 370
8.4.1 Variable Elimination for Belief Networks. 372
8.4.2 Representing Conditional Probabilities and Factors . . . 381
8.5 Sequential Probability Models. 384
8.5.1 Markov Chains. 384
8.5.2 Plidden Markov Models. 387
X
Contents
8.5.3 Algorithms for Monitoring and Smoothing. 392
8.5.4 Dynamic Belief Networks. 393
8.5.5 Time Granularity. 394
8.5.6 Probabilistic Models of Language . 395
8.6 Stochastic Simulation. 402
8.6.1 Sampling from a Single Variable. 403
8.6.2 Forward Sampling in Belief Networks. 404
8.6.3 Rejection Sampling. 405
8.6.4 Likelihood Weighting. 407
8.6.5 Importance Sampling . 408
8.6.6 Particle Filtering. 410
8.6.7 Markov Chain Monte Carlo. 412
8.7 Review. 414
8.8 References and Further Reading . 414
8.9 Exercises. 415
9 Planning with Uncertainty 425
9.1 Preferences and Utility. 426
9.1.1 Axioms for Rationality. 426
9.1.2 Factored Utility. 433
9.1.3 Prospect Theory. 435
9.2 One-Off Decisions. 438
9.2.1 Single-Stage Decision Networks. 442
9.3 Sequential Decisions. 444
9.3.1 Decision Networks. 445
9.3.2 Policies. 449
9.3.3 Variable Elimination for Decision Networks . 451
9.4 The Value of Information and Control. 455
9.5 Decision Processes. 458
9.5.1 Policies. 462
9.5.2 Value Iteration . 464
9.5.3 Policy Iteration. 468
9.5.4 Dynamic Decision Networks. 470
9.5.5 Partially Observable Decision Processes. 474
9.6 Review. 475
9.7 References and Further Reading. 476
9.8 Exercises. 476
10 Learning with Uncertainty 487
10.1 Probabilistic Learning. 487
10.1.1 Learning Probabilities. 488
10.1.2 Probabilistic Classifiers . 491
10.1.3 MAP Learning of Decision Trees. . 496
Contents
xi
10.1.4 Description Length. 498
10.2 Unsupervised Learning . 499
10.2.1 k-Means. 499
10.2.2 Expectation Maximization for Soft Clustering . 503
10.3 Learning Belief Networks. 507
10.3.1 Learning the Probabilities. 508
10.3.2 Hidden Variables. 509
10.3.3 Missing Data . 509
10.3.4 Structure Learning. 510
10.3.5 General Case of Belief Network Learning. 512
10.4 Bayesian Learning . 512
10.5 Review. 517
10.6 References and Further Reading . 518
10.7 Exercises. 518
11 Multiagent Systems 521
11.1 Multiagent Framework. 521
11.2 Representations of Games. 523
11.2.1 Normal Form Games. 523
11.2.2 Extensive Form of a Game. 524
11.2.3 Multiagent Decision Networks. 527
11.3 Computing Strategies with Perfect Information. 528
11.4 Reasoning with Imperfect Information. 532
11.4.1 Computing Nash Equilibria. 538
11.5 Group Decision Making. 541
11.6 Mechanism Design. 542
11.7 Review. 544
11.8 References and Further Reading . 545
11.9 Exercises. 545
12 Learning to Act 549
12.1 Reinforcement Learning Problem. 549
12.2 Evolutionary Algorithms . 553
12.3 Temporal Differences. 554
12.4 Q-learning. 555
12.5 Exploration and Exploitation. 557
12.6 Evaluating Reinforcement Learning Algorithms. 559
12.7 On-Policy Learning. 560
12.8 Model-Based Reinforcement Learning. 562
12.9 Reinforcement Learning with Features. 565
12.9.1 SARSA with Linear Function Approximation. 565
12.10 Multiagent Reinforcement Learning. 569
12.10.1 Perfect-Information Games. 569
Xll
Contents
12.10.2 Learning to Coordinate . 569
12.11 Review. 574
12.12 References and Further Reading . 574
12.13 Exercises. 575
IV Reasoning, Learning and Acting with Individuals and Rela-
tions 579
13 Individuals and Relations 581
13.1 Exploiting Relational Structure. 582
13.2 Symbols and Semantics . 583
13.3 Datalog: A Relational Rule Language. 584
13.3.1 Semantics of Ground Datalog. 587
13.3.2 Interpreting Variables. 589
13.3.3 Queries with Variables. 595
13.4 Proofs and Substitutions. 597
13.4.1 Instances and Substitutions. 597
13.4.2 Bottom-up Procedure with Variables. 599
13.4.3 Unification. 601
13.4.4 Definite Resolution with Variables. 602
13.5 Function Symbols. 604
13.5.1 Proof Procedures with Function Symbols. 610
13.6 Applications in Natural Language. 612
13.6.1 Using Definite Clauses for Context-Free Grammars . . . 614
13.6.2 Augmenting the Grammar. 618
13.6.3 Building Structures for Non-terminals . 619
13.6.4 Canned Text Output. 619
13.6.5 Enforcing Constraints. 620
13.6.6 Building a Natural Language Interface to a Database . . 621
13.6.7 Limitations . 627
13.7 Equality . 628
13.7.1 Allowing Equality Assertions. 629
13.7.2 Unique Names Assumption. 630
13.8 Complete Knowledge Assumption. 633
13.8.1 Complete Knowledge Assumption Proof Procedures . . 637
13.9 Review. 638
13.10 References and Further Reading. 638
13.11 Exercises. 639
14 Ontologies and Knowledge-Based Systems 645
14.1 Knowledge Sharing. 645
14.2 Flexible Representations. 646
Contents xiii
14.2.1 Choosing Individuals and Relations. 647
14.2.2 Graphical Representations. 650
14.2.3 Classes . 652
14.3 Ontologies and Knowledge Sharing. 655
14.3.1 Uniform Resource Identifiers. 661
14.3.2 Description Logic. 662
14.3.3 Top-Level Ontologies . 670
14.4 Implementing Knowledge-Based Systems. 673
14.4.1 Base Languages and Metalanguages. 674
14.4.2 A Vanilla Meta-Interpreter. 676
14.4.3 Expanding the Base Language. 678
14.4.4 Depth-Bounded Search . 680
14.4.5 Meta-Interpreter to Build Proof Trees . 681
14.4.6 Delaying Goals. 682
14.5 Review. 684
14.6 References and Further Reading . 684
14.7 Exercises. 685
15 Relational Planning, Learning, and Probabilistic Reasoning 691
15.1 Planning with Individuals and Relations . 692
15.1.1 Situation Calculus. 692
15.1.2 Event Calculus. 699
15.2 Relational Learning. 701
15.2.1 Structure Learning: Inductive Logic Programming . 701
15.2.2 Learning Hidden Properties: Collaborative Filtering . . . 706
15.3 Statistical Relational Artificial Intelligence. 711
15.3.1 Relational Probabilistic Models. 711
15.4 Review. 724
15.5 References and Further Reading . 724
15.6 Exercises. 725
V Retrospect and Prospect 729
16 Retrospect and Prospect 731
16.1 Dimensions of Complexity Revisited. 731
16.2 Social and Ethical Consequences. 736
16.3 References and Further Reading . 742
16.4 Exercises. 742
A Mathematical Preliminaries and Notation 745
A.l Discrete Mathematics. 745
A.2 Functions, Factors and Arrays. 746
XIV
Contents
A.3 Relations and the Relational Algebra. 747
References 751
Index
773 |
| any_adam_object | 1 |
| author | Poole, David L. 1958- Mackworth, Alan K. 1945- |
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| dewey-ones | 006 - Special computer methods |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition |
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| indexdate | 2024-08-27T00:18:50Z |
| institution | BVB |
| isbn | 9781107195394 110719539X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029817761 |
| oclc_num | 1021103023 |
| open_access_boolean | |
| owner | DE-20 DE-11 DE-703 DE-29 DE-355 DE-BY-UBR DE-12 DE-573 DE-M468 DE-523 DE-1102 DE-860 DE-83 |
| owner_facet | DE-20 DE-11 DE-703 DE-29 DE-355 DE-BY-UBR DE-12 DE-573 DE-M468 DE-523 DE-1102 DE-860 DE-83 |
| physical | xxviii, 792 Seiten Illustrationen, Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| 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, Canada), Alan K. Mackworth (University of British Columbia, Canada) Second edition Cambridge, United Kingdom ; New York, NY ; Port Melbourne ; New Delhi ; Singapore Cambridge University Press 2017 xxviii, 792 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Computational intelligence Textbooks Artificial intelligence Textbooks Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Informatik (DE-588)4026894-9 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 DE-188 Mackworth, Alan K. 1945- Verfasser (DE-588)141818115 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029817761&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 Künstliche Intelligenz (DE-588)4033447-8 gnd Informatik (DE-588)4026894-9 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4026894-9 (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_full | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Canada), Alan K. Mackworth (University of British Columbia, Canada) |
| title_fullStr | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Canada), Alan K. Mackworth (University of British Columbia, Canada) |
| title_full_unstemmed | Artificial intelligence foundations of computational agents David L. Poole (University of British Columbia, Canada), Alan K. Mackworth (University of British Columbia, Canada) |
| 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 Künstliche Intelligenz (DE-588)4033447-8 gnd Informatik (DE-588)4026894-9 gnd |
| topic_facet | Computational intelligence Textbooks Artificial intelligence Textbooks Künstliche Intelligenz Informatik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029817761&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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