Advanced Artificial Intelligence (Series on Intelligence Science)

Contents......Page 12
Preface......Page 6
Acknowledgement......Page 9
1.1 Brief History of AI......Page 18
1.2 Cognitive Issues of AI......Page 22
1.3 Hierarchical Model of Thought......Page 24
1.4 Symbolic Intelligence......Page 26
1.5.1 Cognitive School......Page 29
1.5.2 Logical School......Page 30
1.5.3 Behavioral School......Page 31
1.6 Automated Reasoning......Page 32
1.7 Machine Learning......Page 35
1.8 Distributed Artificial Intelligence......Page 37
1.9 Artificial Thought Model......Page 41
1.10 Knowledge Based Systems......Page 42
Exercises......Page 46
2.1 Introduction......Page 47
2.2 Logic Programming......Page 50
2.2.1 Definitions of logic programming......Page 51
2.2.2 Data structure and recursion in Prolog......Page 52
2.2.3 SLD resolution......Page 53
2.2.4 Non-logic components: CUT......Page 57
2.3 Nonmonotonic Logic......Page 62
2.4 Closed World Assumption......Page 64
2.5 Default Logic......Page 67
2.6 Circumscription Logic......Page 73
2.7 Nonmonotonic Logic NML......Page 77
2.8.1 Moore System......Page 80
2.8.2 O Logic......Page 81
2.8.3 Theorems on normal forms......Page 83
2.8.4 - mark and a kind of course of judging for stable expansion......Page 85
2.9 Truth Maintenance System......Page 89
2.10 Situation Calculus......Page 95
2.10.1 Many-sorted logic for situation calculus......Page 96
2.10.2 Basic action theory in LR......Page 97
2.11.1 Frame Axiom......Page 99
2.11.2 Criteria for a solution to the frame problem......Page 102
2.11.3 Nonmonotonic solving approach of the frame problem......Page 104
2.12.1 Description Logic......Page 112
2.12.2 Syntax of dynamic description logic......Page 115
2.12.3 Semantics of dynamic description logic......Page 118
Exercises......Page 122
3.1 Introduction......Page 124
3.2 Backtracking......Page 131
3.3 Constraint Propagation......Page 133
3.5 Intelligent Backtracking and Truth Maintenance......Page 136
3.6 Variable Instantiation Ordering and Assignment Ordering......Page 138
3.8 Graph-based Backjumping......Page 139
3.9 Influence-based Backjumping......Page 141
3.10.1 Unit Sharing Strategy for Identical Relation......Page 146
3.10.2 Interval Propagation......Page 148
3.10.3 Inequality Graph......Page 149
3.10.4 Inequality Reasoning......Page 150
3.11 Constraint Reasoning System COPS......Page 151
3.12 ILOG Solver......Page 156
Exercise......Page 163
4.1 Introduction......Page 164
4.2 Basic approaches in qualitative reasoning......Page 165
4.3 Qualitative Model......Page 167
4.4 Qualitative Process......Page 170
4.5 Qualitative Simulation Reasoning......Page 174
4.5.1 Qualitative state transformation......Page 175
4.5.2 QSIM algorithm......Page 176
4.6 Algebra Approach......Page 178
4.7 Spatial Geometric Qualitative Reasoning......Page 180
4.7.1 Spatial logic......Page 181
4.7.2 Temporal spatial relation......Page 183
4.7.3. Applications of temporal and spatial logic......Page 185
4.7.4. Randell algorithm......Page 186
Exercises......Page 187
5.1 Overview......Page 188
5.2 Basic Notations......Page 190
5.3 Process Model......Page 192
5.4 Case Representation......Page 196
5.4.1 Semantic Memory Unit......Page 197
5.4.2 Memory Network......Page 198
5.5 Case Indexing......Page 201
5.6 Case Retrieval......Page 202
5.7 Similarity Relations in CBR......Page 205
5.7.2 Structural similarity......Page 206
5.7.3 Goal’s features......Page 207
5.7.5 Similarity assessment......Page 208
5.8 Case Reuse......Page 211
5.9 Case Retainion......Page 213
5.10 Instance-Based Learning......Page 214
5.10.1 Learning tasks of IBL......Page 215
5.10.2 Algorithm IB1......Page 216
5.10.3 Reducing storage requirements......Page 217
5.11 Forecast System for Central Fishing Ground......Page 220
5.11.1 Problem Analysis and Case Representation......Page 221
5.11.2 Similarity Measurement......Page 223
5.11.3 Indexing and Retrieval......Page 224
5.11.4 Revision with Frame......Page 226
5.11.5 Experiments......Page 229
Exercises......Page 230
6.1.1 History of Bayesian theory......Page 231
6.1.2 Basic concepts of Bayesian method......Page 232
6.1.3 Applications of Bayesian network in data mining......Page 233
6.2.1 Foundation of probability theory......Page 236
6.2.2 Bayesian probability......Page 239
6.3 Bayesian Problem Solving......Page 242
6.3.1 Common methods for prior distribution selection......Page 243
6.3.2 Computational learning......Page 247
6.3.3 Steps of Bayesian problem solving......Page 249
6.4 Naïve Bayesian Learning Model......Page 251
6.4.1 Naïve Bayesian learning model......Page 252
6.4.2 Boosting of naïve Bayesian model......Page 255
6.4.3 The computational complexity......Page 257
6.5.1 Structure of Bayesian network and its construction......Page 258
6.5.2 Probabilistic distribution of learning Bayesian network......Page 260
6.5.3 Structure of learning Bayesian network......Page 262
6.6 Bayesian Latent Semantic Model......Page 266
6.7.1 Web page clustering......Page 270
6.7.2 Label documents with latent classification themes......Page 271
6.7.3 Learning labeled and unlabeled data based on naïve Bayesian model......Page 273
Exercises......Page 276
7.1 Introduction......Page 277
7.2.1 Inductive general paradigm......Page 279
7.2.2 Conditions of concept acquisition......Page 281
7.2.3 Background knowledge of problems......Page 282
7.2.4 Selective and constructive generalization rules......Page 284
7.3 Inductive Bias......Page 287
7.4 Version Space......Page 289
7.4.1 Candidate-elimination algorithm......Page 290
7.4.2 Two improved algorithms......Page 293
7.5 AQ Algorithm for Inductive Learning......Page 295
7.6 Constructing Decision Trees......Page 296
7.7 ID3 Learning Algorithm......Page 297
7.7.1 Introduction to information theory......Page 298
7.7.2 Attribute selection......Page 299
7.7.4 Application example of ID3 algorithm......Page 300
7.7.5 Dispersing continuous attribute......Page 303
7.8 Bias Shift Based Decision Tree Algorithm......Page 304
7.8.1 Formalization of bias......Page 305
7.8.2 Bias shift representation......Page 306
7.8.3 Algorithms......Page 307
7.8.4 Procedure bias shift......Page 309
7.8.5 Bias shift based decision tree learning algorithm BSDT......Page 312
7.8.6 Typical case base maintain algorithm TCBM......Page 313
7.8.7 Bias feature extracting algorithm......Page 314
7.8.8 Improved decision tree generating algorithm GSD......Page 315
7.8.9 Experiment results......Page 317
7.9.1 Gold’s learning theory......Page 319
7.9.2 Model inference......Page 321
7.9.3 Valiant’s learning theory......Page 322
Exercises......Page 324
8.1.1 Empirical risk......Page 326
8.1.2 VC Dimension......Page 327
8.2.1 Classical definition of learning consistency......Page 328
8.2.3 VC entropy......Page 329
8.3 Structural Risk Minimization Inductive Principle......Page 331
8.4.1 Linearly separable case......Page 334
8.4.2 Linearly non-separable case......Page 337
8.5.2 Radial Basis Function......Page 340
8.5.4 Dynamic kernel function......Page 341
Exercises......Page 343
9.1 Introduction......Page 345
9.2 Model for EBL......Page 346
9.3.1 Basic principle......Page 348
9.3.2 Interchange with explanation and generalization......Page 353
9.4 Explanation Generalization using Global Substitutions......Page 354
9.5 Explanation-Based Specialization......Page 357
9.6.1 Operational principle......Page 361
9.6.2 Meta Explanation......Page 362
9.6.3 An example......Page 363
9.7 SOAR Based on Memory Chunks......Page 365
9.8 Operationalization......Page 368
9.8.1 Utility of PRODIGY......Page 371
9.8.3 Operationality of MRS-EBG......Page 372
9.9.1 Imperfect domain theory......Page 373
9.9.2 Inverting Resolution......Page 374
9.9.3 Deep knowledge based approach......Page 377
Exercises......Page 378
10.1 Introduction......Page 379
10.2 Reinforcement Learning Model......Page 382
10.3 Dynamic Programming......Page 386
10.4 Monte Carlo Methods......Page 387
10.5 Temporal-Difference Learning......Page 390
10.6 Q-Learning......Page 395
10.7 Function Approximation......Page 398
10.8 Reinforcement Learning Applications......Page 400
Exercises......Page 403
11.1 Introduction......Page 404
11.1.1 Categorized View of Knowledge......Page 407
11.1.2 A New Type of Membership Relations......Page 408
11.1.3 The View of Concept’s Boundary......Page 409
11.2.1 General Reduction......Page 410
11.2.2 Relative Reduction......Page 411
11.2.3 Dependency of Knowledge......Page 413
11.3.1 Formal Definition of Decision Table......Page 414
11.3.2 Decision Logic Language......Page 415
11.3.3 Semantics of Decision Logic Language......Page 416
11.3.4 Deduction of Decision Logic......Page 418
11.3.6 Decision Rules and Algorithms......Page 420
11.3.7 Inconsistent and Indiscernibility of Decision......Page 421
11.4.1 Dependency of Attributes......Page 422
11.4.2 Reduction of Consistent Decision Tables......Page 423
11.4.3 Reduction of Inconsistent Decision Tables......Page 430
11.5 Extended Model of Rough Sets......Page 436
11.5.1 Variable Precision Rough Set Model......Page 437
11.5.2 Similarity Based Model......Page 438
11.5.3 Rough Set Based Nonmonotonic Logic......Page 439
11.6 Experimental Systems of Rough Sets......Page 440
11.7 Granular Computing......Page 442
11.8 Future Trends of Rough Set Theory......Page 444
Exercises......Page 446
12.1 Introduction......Page 447
12.2 The Apriori Algorithm......Page 451
12.3 FP-Growth Algorithm......Page 454
12.4 CFP-Tree Algorithm......Page 458
12.5 Mining General Fuzzy Association Rules......Page 461
12.6 Distributed Mining Algorithm For Association Rules......Page 465
12.6.1 Generation of candidate sets......Page 467
12.6.2 Local pruning of candidate sets......Page 468
12.6.3 Global pruning of candidate sets......Page 470
12.6.4 Count polling......Page 471
12.6.5 Distributed mining algorithm of association rules......Page 472
12.7 Parallel Mining of Association Rules......Page 475
12.7.1 Count Distribution Algorithm......Page 476
12.7.2 Fast Parallel Mining Algorithm......Page 477
12.7.3 DIC-based algorithm......Page 478
12.7.4 Data skewness and workload balance......Page 480
Exercises......Page 482
13.1 Introduction......Page 484
13.2 Formal Model of Evolution System Theory......Page 486
13.3 Darwin's Evolutionary Algorithm......Page 489
13.4 Classifier System......Page 490
13.5 Bucket Brigade Algorithm......Page 496
13.6 Genetic Algorithm......Page 497
13.6.1 Major steps of genetic algorithm......Page 499
13.6.2 Representation schema......Page 500
13.6.3 Crossover operation......Page 502
13.6.4 Mutation operation......Page 504
13.7 Parallel Genetic Algorithm......Page 505
13.8 Classifier System Boole......Page 506
13.9 Rule Discovery System......Page 510
13.11 Evolutionary Programming......Page 514
Exercises......Page 515
14.1 Introduction......Page 516
14.2.1 The concept of agent......Page 519
14.2.1 Rational agent......Page 521
14.3.1 Agent basic architecture......Page 522
14.3.2 Deliberative agent......Page 524
14.3.3 Reactive agent......Page 527
14.3.4 Hybrid Agent......Page 528
14.4 Agent Communication Language ACL......Page 531
14.4.1 Agent Communication introduction......Page 532
14.4.2 FIPA ACL message......Page 535
14.5.1 Introduction......Page 541
14.5.2 Contract net protocol......Page 544
14.5.3 Partial global planning......Page 547
14.5.4 The Planning based on constraint propagation......Page 550
14.5.5 Ecological based cooperation......Page 555
14.5.6 Game theory based negotiation......Page 557
14.5.8 Team-oriented collaboration......Page 558
14.6 Mobile Agent......Page 560
14.7.1 The architecture of MAGE......Page 563
14.7.3 Visual agent development tool......Page 564
14.7.4 MAGE running platform......Page 566
14.8 Agent Grid Intelligence Platform......Page 567
Exercises......Page 568
15.1 Introduction......Page 570
15.2 Exploration of Artificial Life......Page 576
15.3 Artificial Life Model......Page 577
15.4 Research Approach of Artificial Life......Page 581
15.5 Cellular Automata......Page 585
15.6 Morphogenesis Theory......Page 588
15.7 Chaos Theories......Page 591
15.8.1 Digital life evolutionary model Tierra......Page 592
15.8.2 Avida......Page 594
15.8.3 Ecosystem for biological breed Terrarium......Page 596
15.8.4 Artificial fish......Page 597
15.8.5 Autolife......Page 598
Exercises......Page 599
References......Page 602