Advanced artificial intelligence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hackensack, NJ [u.a.]
World Scientific
2011
|
| Schriftenreihe: | Series on intelligence science
1 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 613 S. Ill., graph. Darst. |
| ISBN: | 9789814291347 981429134X |
Internformat
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Datensatz im Suchindex
| _version_ | 1804149689609617409 |
|---|---|
| adam_text | Titel: Advanced artificial intelligence
Autor: Shi, Zhongzhi
Jahr: 2011
Contents
Preface v
Acknowledgement ix
Chapter 1 Introduction 1
1.1 Brief History of AI 1
1.2 Cognitive Issues of AI 5
1.3 Hierarchical Model of Thought 7
1.4 Symbolic Intelligence 9
1.5 Research Approaches of Artificial Intelligence 12
1.6 Automated Reasoning 15
1.7 Machine learning 18
1.8 Distributed Artificial Intelligence 20
1.9 Artificial Thought Model 24
1.10 Knowledge Based Systems 25
Exercises 29
Chapter 2 Logic Foundation of Artificial Intelligence 30
2.1 Introduction ^0
2.2 Logic Plx gramrning 33
2.3 Nonmonotonic Logic **?*
2.4 Closed World Assumption 47
2.5 Default Logic 50
2.6 Ckcumscription Logic 5
2.7 Nonmonotonic Logic NML °
2.8 Autoepistemic Logic 3
xii Contents
2.9 Truth Maintenance System 72
2.10 Situation Calculus 78
2.11 Frame Problem 82
2.12 Dynamic Description Logic 95
Exercises 105
Chapter 3 Constraint Reasoning 107
3.1 Introduction 107
3.2 Backtracking 114
3.3 Constraint Propagation 116
3.4 Constraint Propagation in Tree Search 119
3.5 Intelligent Backtracking and Truth Maintenance 119
3.6 Variable Instantiation Ordering and Assignment Ordering 121
3.7 Local Revision Search 122
3.8 Graph-based Backjumping 122
3.9 Infiuence-based Backjumping 124
3.10 Constraint Relation Processing 129
3.11 Constraint Reasoning System COPS 134
3.12 ILOG Solver 139
Exercise 146
Chapter 4 Qualitative Reasoning 147
4.1 Introduction 147
4.2 Basic approaches in qualitative reasoning 148
4.3 Qualitative Model 150
4.4 Qualitative Process 153
4.5 Qualitative Simulation Reasoning 157
4.6 Algebra Approach 161
4.7 Spatial Geometrie Qualitative Reasoning 163
Exercises 170
Chapter 5 Case-Based Reasoning 171
5.1 Overview 171
5.2 Basic Notarions 173
5.3 Process Model 175
Contents xiii
5.4 Case Representation 179
5.5 Case Indexing 184
5.6 Case Retrieval 185
5.7 Similarity Relations in CBR 188
5.8 Case Reuse 194
5.9 Case Retainion 196
5.10 Instance-Based Learning 197
5.11 Forecast System for Central Fishing Ground 203
Exercises 213
Chapter 6 Probabilistic Reasoning 214
6.1 Introduction 214
6.2 Foundation of Bayesian Probability 219
6.3 Bayesian Problem Solving 225
6.4 Naive Bayesian Learning Model 234
6.5 Construction of Bayesian Network 241
6.6 Bayesian Latent Semantic Model 249
6.7 Semi-supervised Text Mining Algorithrns 253
Exercises 259
Chapter 7 Inductive Learning 260
7.1 Introduction 260
7.2 Logic Foundation of Inductive Learning 262
7.3 Inductive Bias 270
7.4 Version Space 272
7.5 AQAlgoritmnformductive Learning 278
7.6 Constructing Decision Trees 279
7.7 JD3 Learning Algorithm 280
7.8 Bias Shirt Based Decision Tree Algorithm 287
7.9 Computational Theories of Inductive Learning 302
Exercises 307
Chapter 8 Support Vector Machine 309
8.1 Statistical Learning Problem 309
8.2 Consistency of Learning Processes 31 *
xiv Contents
8.3 Structural Risk Minimization Inductive Principle 314
8.4 Support Vector Machine 317
8.5 Kernel Function 323
Exercises 326
Chapter 9 Explanation-Based Learning 328
9.1 Introduction 328
9.2 Model forEBL 329
9.3 Explanation-Based Generalization 331
9.4 Explanation Generalization using Global Substitutions 337
9.5 Explanation-Based Specialization 340
9.6 Logic Program of Explanation-Based Generalization 344
9.7 SOAR Based on Memory Chunks 348
9.8 Operationalization 351
9.9 EBL with imperfect domain theory 356
Exercises 361
Chapter 10 Reinforcement Learning 362
10.1 Introduction 362
10.2 Reinforcement Learning Model 365
10.3 Dynamic Prograrmrting 369
10.4 Monte Carlo Methods 370
10.5 Temporal-Difference Learning 373
10.6 Q-Learning 378
10.7 Function Approximation 381
10.8 Reinforcement Learning Applications 383
Exercises 386
Chapter 11 Rough Set 387
11.1 Introduction 387
11.2 Reduction of Knowledge 393
11.3 Decision Logic 397
11.4 Reduction of Decision Tables 405
11.5 Extended Model of Rough Sets 419
11.6 Experimental Systems of Rough Sets 423
Contents xv
11.7 Granulär Computing 425
11.8 Future Trends of Rough Set Theory 427
Exercises 429
Chapter 12 Association Rules 430
12.1 Introduction 430
12.2 The Apriori Algorithm 434
12.3 FP-Growth Algorithm 437
12.4 CFP-Tree Algorithm 441
12.5 Mining General Fuzzy Association Rules 444
12.6 Distributed Mining Algorithm For Association Rules 448
12.7 Parallel Mining of Association Rules 458
Exercises 465
Chapter 13 Evolutionary Computation 467
13.1 Introduction 467
13.2 Formal Model of Evolution System Theory 469
13.3 Darwin s Evolutionary Algorithm 472
13.4 Classifier System 473
13.5 Bücket Brigade Algorithm 479
13.6 Genetic Algorithm 480
13.7 Parallel Genetic Algorithm 488
13.8 Classifier System Boole 489
13.9 Rule Discovery System 493
13.10 Evolutionary Strategy 497
13.11 Evolutionary Progranmiing
Exercises
497
498
Chapter 14 Distributed Intelligence 499
14.1 Introduction
14.2 The Essence of Agent
14.3 Agent Architecture
14.4 Agent Communication Language ACL
14.5 Coordination and Cooperation
14.6 Mobile Agent
502
505
514
524
543
xvi Contents
14.7 Multi-Agent Environment MAGE 546
14.8 Agent Grid Intelligence Platform 550
Exercises 551
Chapter 15 Artificial Life 553
15.1 Introduction 553
15.2 Exploration of Artificial Life 559
15.3 Artificial Life Model 560
15.4 Research Approach of Artificial Life 564
15.5 Cellular Automata 568
15.6 Morphogenesis Theory 571
15.7 Chaos Theories 574
15.8 Experimental Systems of Artificial Life 575
Exercises 582
References 585
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| illustrated | Illustrated |
| indexdate | 2024-07-10T00:27:01Z |
| institution | BVB |
| isbn | 9789814291347 981429134X |
| language | English |
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| spelling | Shi, Zhongzhi Verfasser (DE-588)1014634504 aut Advanced artificial intelligence Zhongzhi Shi Hackensack, NJ [u.a.] World Scientific 2011 XVI, 613 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Series on intelligence science 1 Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Series on intelligence science 1 (DE-604)BV040664920 1 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025427941&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shi, Zhongzhi Advanced artificial intelligence Series on intelligence science Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 |
| title | Advanced artificial intelligence |
| title_auth | Advanced artificial intelligence |
| title_exact_search | Advanced artificial intelligence |
| title_full | Advanced artificial intelligence Zhongzhi Shi |
| title_fullStr | Advanced artificial intelligence Zhongzhi Shi |
| title_full_unstemmed | Advanced artificial intelligence Zhongzhi Shi |
| title_short | Advanced artificial intelligence |
| title_sort | advanced artificial intelligence |
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| topic_facet | Künstliche Intelligenz |
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