MICAI 2005: Advances in Artificial Intelligence: 4th Mexican International Conference on Artificial Intelligence, Monterrey, Mexico, November 14-18, 2005,

Front matter......Page 1
Introduction......Page 25
Association Networks: The Learning Algorithm......Page 26
First Experiment: The ``World Graph"......Page 29
Second Experiment: A Case of ``Real Life"......Page 30
Phase Transition Diagrams......Page 31
Clustering and Classification......Page 32
Discussion......Page 33
Introduction......Page 35
Characteristic of Neuro-symbolic Hybrid Systems......Page 36
Compilation Algorithm......Page 37
Implementation of the Symbolic Compiler......Page 39
Test Using the NSHS for the Integration and the Refinement of Knowledge......Page 40
Conclusions......Page 42
References......Page 43
Introduction......Page 45
Extending the Language......Page 47
The New Logic......Page 49
Concluding Remarks......Page 53
Introduction......Page 55
Preliminaries and Terminology......Page 57
Defining Belief Revision Operator......Page 59
Examples......Page 60
Absorbing New Knowledge......Page 61
Postulates......Page 62
Related Work......Page 63
Conclusions and Future Work......Page 64
Introduction......Page 65
Causal Cognitive Maps......Page 66
Fuzzy Cognitive Maps......Page 68
Probabilistic Cognitive Maps......Page 69
A Case of Use of Cognitive Maps......Page 70
Causal Cognitive Map-Based Student Model......Page 71
Fuzzy Cognitive Map-Based Student Model......Page 72
Conclusions......Page 73
References......Page 74
Introduction......Page 75
Temporal Algebras......Page 76
Reasoning Tasks......Page 77
Disjunctive Relations Proposed for Annotation Task......Page 79
Coding the Network of Fuzzy Relations......Page 81
Knowledge Reconstruction......Page 82
Perspectives......Page 83
Introduction......Page 85
EventNet......Page 86
EventNet Inference Algorithm......Page 87
EventNet Temporal Toolkit......Page 88
Reducing Complexity of Consumer Electronics Interfaces......Page 89
Kitchen Scheduler......Page 90
Related Work......Page 91
References......Page 92
Introduction......Page 94
Similarity......Page 95
Uncertainty......Page 97
System Architecture......Page 98
Test Bed......Page 100
Related Work......Page 101
References......Page 102
Introduction......Page 104
Related Works......Page 105
Our Three-Level Approach......Page 106
A Case Study......Page 108
References......Page 112
Introduction......Page 114
The MDKT Project......Page 115
The Competences Ontology......Page 116
Documents Dissemination......Page 117
Maintenance System of the Domain Ontology......Page 118
Conclusion and Future Work......Page 120
References......Page 121
Introduction and Motivations......Page 122
Basic Concepts......Page 123
Modelling Knowledge Distribution, Power and Trust......Page 125
A Worked Example......Page 127
Related Work and Conclusions......Page 130
Analytic Models and CSCL......Page 133
DLV......Page 134
Pedagogical Organization of the Domain Knowledge......Page 135
Modelling the Learner and Her Learning Opportunities......Page 136
Social Knowledge Frontier......Page 137
Planning Individual and Collaborative Learning Activities......Page 138
Learning Task Proposals from the Agent......Page 139
Conclusions......Page 141
References......Page 142
Introduction......Page 143
The Situation Calculus......Page 144
Formulae Representation in the Situation Calculus......Page 147
Deductive Systems' Representation in the Situation Calculus......Page 149
The Incompleteness Result......Page 152
Conclusions......Page 154
Introduction......Page 156
The Hypercube as a Linear Space......Page 157
Problem SAT......Page 158
Solutions of #kSAT for CF's with Just Two Clauses......Page 159
k-CF's with an Arbitrary Number of Clauses......Page 160
Some Set Classes......Page 161
Hypergraph of k-Dimensional Varieties......Page 162
Orders of Sets......Page 164
Conclusions......Page 165
Introduction......Page 166
Preliminaries......Page 167
Forbus Operator......Page 168
Rewriting Forbus Operator with Prime Implicants......Page 169
Another Notion of Minimal Change......Page 171
A New Distance......Page 173
Results......Page 174
Conclusion......Page 175
Introduction......Page 176
Cut Concepts......Page 177
Axioms and Assertions......Page 178
A Quick Look to Fuzzy ALCH......Page 179
Translation from Fuzzy ALCH to EFALCH......Page 180
Reasoning Within EFALCH......Page 181
References......Page 185
Introduction......Page 186
Motivations......Page 187
The SOLVE Component......Page 189
The ANALYSE Component......Page 191
The UPDATE Component......Page 192
A Practical Approach......Page 193
Experimental Results......Page 194
Conclusion, Discussion, and Future Work......Page 196
Introduction......Page 199
Verilog Modeling......Page 200
Continuous Assignment......Page 201
Finite State Machines Representation......Page 202
Predicate Abstraction......Page 203
CLP Constraints Generation......Page 204
Implementation and Experimental Results......Page 206
References......Page 207
Introduction......Page 209
The Particular Problem and the Proposed Solution Model......Page 210
The Grouping Genetic Algorithm......Page 211
Modification and Implementation of Heuristic DJD......Page 212
Experiments and Results......Page 213
Conclusions and Future Work......Page 217
Introduction......Page 219
Radial Search......Page 220
Generation of New Solutions: Two Methods......Page 223
Comparison with Other Known Methods......Page 224
Computational Experiments......Page 225
Conclusions......Page 226
Introduction......Page 228
ISs and Rough Sets......Page 229
Fuzzy Set-Valued ISs(FSVISs)......Page 230
Approximating Concepts Via Rough Sets in FSVISs......Page 231
Decision Rules in FSVISs......Page 233
Conclusion......Page 236
Introduction......Page 238
Basic Definitions......Page 239
Semantics of a Cycle......Page 240
Consistency Checking......Page 242
On Reverting Edges in a Cycle......Page 244
Conclusions and Further Work......Page 245
Introduction......Page 248
Related Work......Page 249
Detailed Description of Fuzzeval......Page 251
Discussion of Results......Page 254
Future Work and Conclusions......Page 256
References......Page 257
Introduction......Page 258
Real-Life Production Scheduling Problem......Page 259
Modelling Uncertain Disruptions......Page 260
Dispatching Rules......Page 262
Simulation of Realised Schedule......Page 263
Predictability Analysis......Page 264
Acknowledgments......Page 266
References......Page 267
Motivation......Page 268
Model Driven ABSS Replication......Page 270
A Replication Case Study......Page 272
Reimplementation Issues......Page 273
References......Page 276
Introduction......Page 278
Communication Policies Among Agents in Swarm Intelligent Systems......Page 279
Background......Page 280
Experiments Setup......Page 281
Direct Information Exchange......Page 283
Indirect Information Exchange......Page 284
Conclusions......Page 286
Introduction......Page 288
The Multiagent Plan Repair Problem......Page 289
Multiagent Plan Repair......Page 290
Experimental Results......Page 293
Discussion......Page 295
Introduction......Page 299
Electronic Institutions Fundamentals......Page 300
Open MAS Approach......Page 301
Architecture......Page 303
Methodology......Page 304
Help-Desk Information System......Page 305
Applying the Framework......Page 306
Closing Remarks......Page 308
Introduction......Page 311
Structure of an Extended Behavior Network......Page 312
Action Selection Algorithm......Page 314
Agent Architecture and Environment......Page 315
Assessing Action Selection Quality......Page 316
The Behavior Network Agent Compared to a Completely Different Agent Based on Finite-State-Machines.......Page 317
The Behavior Network Agent Compared to a Plain Reactive Agent That Uses the Same Sensory-Motor Apparatus......Page 318
Discussion and Conclusion......Page 319
References......Page 320
Introduction......Page 321
Related Works......Page 322
Reducing Waiting Time......Page 323
Reducing Gas Emission......Page 324
Simulation Environment......Page 326
Experimental Results......Page 327
Conclusions and Future Work......Page 329
Introduction......Page 331
Stereo Matching: A Fundamentally Ill-Posed Problem......Page 333
Admissible Point-Wise Correspondences......Page 336
Surfaces for the Corresponding Volumes......Page 337
Conclusion......Page 340
Introduction......Page 342
Description of an Object......Page 343
Basics on Associative Memories......Page 344
Numerical Example......Page 346
Implementation of the Technique......Page 347
Construction of the Memory......Page 348
Results......Page 349
References......Page 350
Introduction......Page 352
Face Segmentation......Page 353
Eyes Location and Tracking......Page 354
Face Feature Extraction......Page 355
Expression Classification Based on Linear Programming......Page 357
Evaluations......Page 358
Conclusions......Page 359
References......Page 360
Introduction......Page 361
Related Works......Page 362
Hardware System......Page 363
People Detection and Tracking Process......Page 364
Creation of the Map of the Environment Hmax......Page 365
Creation of the Occupancy Map O......Page 366
Face Detection......Page 367
Tracking......Page 368
Conclusions and Future Work......Page 369
Introduction......Page 371
Background and Related Work......Page 372
Neuro-vision Systems......Page 373
Computational Neural Networks......Page 374
Our Approach......Page 375
Descriptor Vector Generation and Normalization......Page 377
Experimental Results......Page 379
Conclusions and Future Work......Page 380
References......Page 381
Introduction......Page 383
Previous Work......Page 384
Color Classification......Page 385
Scanlines and Horizon Detection......Page 387
Seed Extraction......Page 388
Results......Page 389
Conclusions and Future Work......Page 391
Introduction......Page 393
Collective Learning Systems......Page 394
CLS Implementation as an ALISA Engine......Page 395
ALISA Configuration......Page 396
Audio Signal Testing......Page 397
Recognition and Segmentation of Celestial Bodies......Page 398
Recognition Animal Sound Spectrograms......Page 400
Conclusions......Page 401
References......Page 402
Introduction......Page 403
Bayesian Texture Classification......Page 404
Segmentation Algorithm......Page 405
Experimental Results......Page 406
Conclusions and Perspectives......Page 408
Introduction......Page 410
Related Works......Page 411
Fast Edge Detection Based on DCT Coefficients......Page 412
Adaptive Selection of Salient Points......Page 415
Similarity Measurement......Page 416
Performance Evaluation of Retrieval System......Page 417
Conclusions......Page 418
Introduction......Page 420
The ISOMAP Algorithm and the Straightforward Method with the Residual Variance......Page 421
Our Method......Page 422
Experimental Results......Page 425
Conclusions......Page 427
Introduction......Page 429
Approximate Proximity Searching......Page 431
Data Organization......Page 432
Index Process......Page 433
Experimental Validation......Page 434
Conclusion and Future Work......Page 437
Introduction......Page 439
Environment......Page 440
Prediction and Learning......Page 441
Decision Making......Page 442
Evaluation of the Agent......Page 444
Comparison of Action-Selection Mechanisms......Page 445
Effect of the Memory Length L......Page 446
Conclusion......Page 447
Introduction......Page 449
Sources of Noise......Page 450
An Example......Page 451
Qualifying Non-random Noise......Page 452
Boolean Differences......Page 453
Empirical Results......Page 454
Conclusion and Future Work......Page 457
Introduction......Page 459
Reduced Support Vector Machines......Page 461
Constructing Standard SVM with Less Support Vectors from Reduced SVM......Page 463
Computational Results......Page 464
References......Page 465
Introduction......Page 467
Description of the Online Tree Method......Page 468
ReviseForgetting Function......Page 469
Experimentation Results with Concepts That Change over Time......Page 471
Gradual Concept Drift......Page 472
Abrupt Concept Drift and Noise......Page 473
Recurring Contexts in Abruptly Changing Concepts......Page 475
Conclusions......Page 476
References......Page 477
Introduction......Page 478
Method by Hastie and Tibshirani......Page 479
Method by Wu, Lin, and Weng......Page 480
A Closer Look at the PWC-CC Algorithm......Page 481
Our Algorithm......Page 482
Experimental Results......Page 483
Conclusion......Page 484
Introduction......Page 486
Support Vector Machine......Page 487
Littlewood-Paley Wavelet Kernel Function......Page 488
Least Squares Littlwood-Paley Support Vector Machine......Page 491
Experiments and Results......Page 492
Conclusion......Page 495
References......Page 496
Introduction......Page 497
Problem Domain......Page 498
Preliminary Definitions......Page 500
Prefix Tree Acceptor......Page 501
Describing the Minimum State Transition Model in MILP......Page 502
Experiment......Page 504
Conclusion......Page 506
Introduction......Page 507
Instruction Selection......Page 509
Instruction Scheduling......Page 511
Register Allocation......Page 512
Future Steps......Page 515
Conclusions......Page 516
Introduction......Page 518
Takagi-Sugeno Fuzzy Model Structure......Page 519
Support Vector Machines......Page 520
Support Vector Machines for Density Estimation......Page 521
General Identification Method......Page 522
Identification Example......Page 523
Conclusions......Page 526
Introduction......Page 528
Neural Network Architecture......Page 530
ARIMA (Box-Jenkins) Model......Page 531
Seasonal vs. Seasonally Adjusted Data......Page 532
Methodology......Page 533
Results......Page 534
Conclusion......Page 535
References......Page 536
Introduction......Page 538
Perception Based Functions in Qualitative Forecasting......Page 539
Forecasting with Perceptual Patterns Defined by MAP......Page 542
Association Rules with Perception Based Trends......Page 545
References......Page 547
Formulation of the Key Equivalence Problem......Page 548
Solution Approach......Page 549
Formalization......Page 550
Detecting Redundancy......Page 551
Experimental Results......Page 552
Improvement: Reducing Sensitivity to Parameter k......Page 553
Reduce the Degree of Each Node......Page 554
Reduce the Length of Branches......Page 555
Conclusion......Page 556
Introduction......Page 558
ISOMAP......Page 559
Our Method......Page 560
Experimental Results......Page 563
Conclusions......Page 565
Introduction......Page 568
Software Component Retrieval Using Conversational CBR......Page 569
A Formal Generalized Case Representation Method......Page 570
Supporting Generalized Case Retrieval Using a Query-Biased Similarity Calculation Method......Page 571
Selecting a Discriminative Question Ranking Metric......Page 572
Information Metric......Page 573
Feature Selection Strategies Metric......Page 574
Conclusion......Page 575
Introduction......Page 578
Approach of Maintaining a Case Library......Page 580
Phase One - Learning Case-Specific Weights......Page 581
Phase Two - Representative Cases Selection Strategy......Page 583
An Example......Page 584
Experimental Analyses......Page 585
References......Page 587
Introduction......Page 589
Discriminative Isometric Feature Mapping......Page 591
Experiments......Page 593
Conclusion......Page 596
Introduction......Page 598
GM(1,1) Grey Forecasting Model......Page 599
Calculate the Transition Probability P......Page 600
The Grey-Markov Forecasting Model for the Electric Power Requirement in China......Page 601
Partition of States by Markov-Chain Forecasting Model......Page 602
Calculate the Transition Probability P......Page 603
Comparison of Forecast Values Between the Grey-Markov Forecasting Model and the GM(1,1) Grey Forecasting Model......Page 604
References......Page 605
Introduction......Page 607
The Proposed Approach......Page 608
The Offline Phase......Page 609
Application in Fault Diagnosis......Page 612
Conclusions and Future Works......Page 615
Introduction......Page 617
A General Framework of Evolutionary Programming Using a Mixed Strategy......Page 618
A Mixed Mutation Strategies Evolutionary Programming Based Species Conservation......Page 620
Experimental Results and Analysis......Page 623
Conclusions......Page 626
Coevolution......Page 627
Description of Our Approach......Page 628
Results......Page 631
Discussion of Results......Page 632
Conclusions and Future Work......Page 635
Introduction......Page 637
The General Procedure......Page 638
The Local Search Operator......Page 639
Partially Matched Crossover......Page 640
Trajectory Crossover......Page 641
Comparison of Recombination Operators......Page 642
Comparison with the Best Known Results......Page 644
Conclusions......Page 645
Introduction......Page 647
Particle Swarm Optimization Technique......Page 648
Proposed PSO Approach for Dealing with Geometrical Place Problems......Page 649
Experiment Results......Page 650
Experiment 1......Page 651
Experiment 2......Page 652
Conclusions......Page 655
Introduction......Page 657
The Form of the PSOOP......Page 658
Global Convergence Analysis of PSOOP......Page 660
Benchmark Functions......Page 661
The Results......Page 662
Conclusion......Page 663
References......Page 664
Introduction......Page 665
The PESO Algorithm......Page 666
Comparisons PESO vs SR vs Toscano's PSO......Page 669
Conclusions and Future Work......Page 670
Introduction......Page 676
Our Approach......Page 677
Experiments and Discussion......Page 679
Conclusions and Future Work......Page 683
Introduction......Page 687
Exploitation in HSMOMA......Page 689
Exploration in HSMOMA......Page 690
Program Implementation of HSMOMA......Page 691
Test Problems and Parameter Settings......Page 692
Results Comparison......Page 693
Conclusions......Page 695
Introduction......Page 697
Evolutionary Multiobjective Optimization Approach for Constructing Ensemble of Intelligent Paradigms......Page 698
Experiment Results......Page 699
Ensemble Design Using MOEA......Page 700
Conclusions......Page 704
Introduction......Page 706
Weighting K-Means......Page 707
Darwinian Evolutionary Approach......Page 708
Darwinian Cooperative Coevolutionary Approach......Page 709
Lamarckian Cooperative Coevolutionary Approach......Page 710
Experiments......Page 712
Conclusion......Page 714
Introduction......Page 716
Linearly Decomposable Non-overlapping Functions......Page 717
Detection of Non-linearities by Random Probes......Page 718
Detecting Non-linearities......Page 719
Processing Non-linearities......Page 720
Algorithm X2......Page 722
Number of Function Evaluations......Page 723
Conclusions and Future Work......Page 724
Introduction......Page 726
K-Means......Page 727
Self Organizing Maps......Page 728
The K Dynamical Self Organizing Maps Model......Page 729
Experiment #1: Computer Generated Data......Page 732
Concluding Remarks......Page 734
Introduction......Page 736
Subsamples Clustered for BP Neural Network Training......Page 737
Learning Subsample Selection......Page 739
BP Neural Network Training......Page 740
Model Validation......Page 741
Acknowledgements......Page 743
References......Page 744
Introduction......Page 745
Literature Review and Conceptual Framework......Page 746
Model and Methodology......Page 747
Research Data......Page 748
Premises for the MLP Optimization......Page 749
Nonlinear Weights of the FDI Determinants: Research Results......Page 751
References......Page 753
Introduction......Page 755
Block Chart of the Method......Page 757
First and Second Order Systems Patterns for Training the Neural Network......Page 759
Results Assessment of the Patterns Recognition......Page 762
References......Page 764
Discourse Plans......Page 765
Dependency Structure Grammars......Page 769
Modular DSG......Page 771
Compiling Modular DSG into Categorial DG......Page 773
Conclusion......Page 775
Introduction......Page 776
The Distributed Architecture of English Text Chunking......Page 778
The Algorithm and Sensitive Features of Each Phrase......Page 779
The Communication Between Agents and the Priority of Each Phrase......Page 780
Experimental Result and Analysis......Page 782
Conclusion and Future Work......Page 783
References......Page 784
Introduction......Page 785
Similarity-Based Ngram Model......Page 786
Word Similarity Calculation......Page 787
Parameter Optimization......Page 788
Evaluation......Page 789
Results and Discussions......Page 790
Conclusions......Page 792
Introduction......Page 794
Named Entity Recognition......Page 795
Voted Co-training Algorithm......Page 796
Experiments and Results......Page 798
Error Analysis of the Detected Entities......Page 799
Co-training for Named Entity Classification (NEC)......Page 801
Conclusions and Future Work......Page 802
Introduction......Page 804
Linguistic Analysis......Page 806
Characteristics of the Corpus......Page 807
Statistical Procedure......Page 808
Discussion......Page 810
Differentiating Idiomatic Expressions......Page 811
Conclusions......Page 812
References......Page 813
Introduction......Page 814
Word Cohesion......Page 815
Numerical Criteria of Word Cohesion......Page 816
Correspondence of Web Statistics to Word Combinations......Page 818
Main Experiment and Comparison of Criteria......Page 819
Other Experimental Results......Page 821
References......Page 822
Introduction......Page 824
Learning Framework......Page 825
Results and Future Work......Page 827
Introduction......Page 830
Support Vector Machines......Page 831
Corpus......Page 832
Feature Vector......Page 833
Experiments......Page 834
Discussion......Page 835
Conclusions......Page 838
Introduction......Page 840
Architecture......Page 841
Passage Ranking......Page 842
Example......Page 843
Experimental Results......Page 844
Conclusions......Page 846
References......Page 847
Introduction......Page 848
Our Methods......Page 849
Experiments and Results......Page 852
Conclusions and Feature Work......Page 854
References......Page 855
Introduction......Page 857
Assumptions......Page 858
Generation of the Domain Dictionary......Page 859
Query Preprocessing......Page 860
Main Algorithm......Page 861
Experimental Results......Page 864
Conclusions......Page 865
Introduction......Page 867
Feature Extraction......Page 868
Basic Notations and Definitions......Page 870
The Network and Markov Chain......Page 872
The Network Learning Algorithm......Page 873
Experimental Results......Page 874
Conclusions and Future Work......Page 875
Introduction......Page 878
VTS Environment Approximation......Page 880
Clean GMM Trained with SOM......Page 881
Noise Estimation......Page 882
Experiments......Page 883
References......Page 885
Introduction......Page 887
Time-Frequency Speech Analysis......Page 888
Digital Speech Signal Processing......Page 889
System Architecture Description......Page 890
Experiments......Page 891
Conclusions......Page 892
Festival......Page 894
Voice Building......Page 896
Experiments......Page 897
Results for Duration Prediction......Page 898
F0 Results......Page 900
Conclusions......Page 902
Introduction......Page 904
Metasymbols......Page 905
Illustration of the Method......Page 908
Common Patterns in Proteins......Page 910
References......Page 912
Introduction......Page 914
Stochastic Logic Programs (SLPs)......Page 916
The Bootstrap for Confidence Estimation......Page 917
Experimental Results and Analysis......Page 919
Conclusion......Page 922
Introduction......Page 924
Previous Work on Multiple Disorder Problem......Page 925
Similarity Measure......Page 926
Inductive Learning of Inductive Rules......Page 927
Using Interaction Rules in Case Interaction Adaptation......Page 928
Evaluation......Page 929
Performance After Using Inductive Rules......Page 930
Conclusion and Future Work......Page 932
Introduction......Page 934
Pixel Classification of Prostate Images......Page 936
Model Fitting to the Binary Image......Page 937
Model Fitting to the Grey Level Image......Page 938
Results......Page 939
Conclusions......Page 940
References......Page 941
Introduction......Page 942
Extra-Cranial Tissue Removing......Page 943
Watershed Algorithm......Page 944
FCM Segmentation......Page 945
The Rule-Based Re-segmentation Processing......Page 946
Results Analyzing......Page 948
Final Results......Page 949
Conclusions......Page 950
References......Page 951
Introduction......Page 952
The Carrel Institution and the Organ Selection and Assignation Process......Page 953
Inference of Arguments......Page 955
Defining Defeat among Arguments and Evaluating the Status of Arguments......Page 957
Use of Argument Schemes and the Role of the Mediator Agent......Page 958
Conclusions and Future Work......Page 960
Introduction......Page 963
Constructive RBF Neural Networks......Page 965
Support Vector Machines......Page 966
NNSRM......Page 967
Data Set......Page 968
Experimental Setup......Page 969
Results and Discussion......Page 970
Conclusions......Page 971
Introduction......Page 973
Infant’s Cry Automatic Recognition Process......Page 974
Mel Frequency Cepstral Coefficients......Page 975
Neural Networks......Page 976
Gradient Descent with Adaptive Learning Rate Back Propagation......Page 977
System Implementation for the Cry Classification......Page 979
Experimental Results......Page 980
Conclusions and Future Works......Page 981
References......Page 982
The hOnGo Robot......Page 983
The Proposal......Page 984
The Paradigm......Page 985
The Control of the Leg......Page 986
The Control of the Steps......Page 987
Concerning Design......Page 988
The Simulation of the Robot......Page 989
First Test......Page 990
Second Test......Page 991
References......Page 992
Introduction......Page 994
Method Description......Page 996
Implementation......Page 999
Experiments......Page 1000
Conclusions and Future Work......Page 1001
Introduction......Page 1004
Sobol Sequence......Page 1005
Randomized Low-Discrepancy Sequences......Page 1006
Expansive and SBL Planners......Page 1007
Experimental Results......Page 1008
Conclusions......Page 1011
Introduction......Page 1014
A General Framework......Page 1015
Object Finding......Page 1016
Expected Value of Time Along any Trajectory......Page 1017
Minimization Using Calculus of Variations......Page 1018
Choosing an Ordering of Regions......Page 1019
Geometric Modeling......Page 1020
Optimal Target and Observer Motions......Page 1021
Discussion and Future Work......Page 1023
Introduction......Page 1025
Landmark Visibility......Page 1027
Landmark Utility......Page 1028
Total Utility......Page 1029
Visual Planning......Page 1030
Results......Page 1032
Conclusions and Future Work......Page 1033
References......Page 1034
Introduction......Page 1036
Dynamic Equations......Page 1037
Reference Trajectory......Page 1038
FWN Control System......Page 1041
Simulations......Page 1043
References......Page 1045
Introduction......Page 1046
Two-Fingered End Effector......Page 1047
Deformation Detection......Page 1048
Performance of the Methodology......Page 1050
Conclusions......Page 1054
Introduction......Page 1056
Neurofuzzy Controller......Page 1057
Reinforcement Learning......Page 1058
Actor-Citric Method......Page 1059
Neuro-Fuzzy-Expert Framework......Page 1060
Determining Reward and Failure Signals......Page 1061
Learning in the Actor......Page 1062
Experimental Set-Up......Page 1063
Conclusions......Page 1064
Introduction......Page 1066
Limitations of Existing Selection Mechanisms......Page 1067
A Semantically-Based Approach......Page 1068
The ISRs Ontologies......Page 1069
Component Selection Steps......Page 1070
An Example of Component Selection......Page 1072
Conclusions......Page 1073
References......Page 1075
Introduction......Page 1076
Related Work......Page 1077
Workplace Description......Page 1078
Pre-configuration......Page 1079
Assembly Operation......Page 1081
Assembly Cycles and Results......Page 1082
References......Page 1084
Introduction......Page 1086
Biology-Inspired Architecture......Page 1087
M2ARTMAP Architecture......Page 1089
Quadruped Mammal Database Simulations......Page 1091
On-Line Control......Page 1092
Conclusions and Future Work......Page 1094
References......Page 1095
Introduction......Page 1097
Robotic System Description......Page 1098
Integration......Page 1100
Implementation......Page 1101
Analysis......Page 1102
Conclusions......Page 1105
Introduction......Page 1106
Structural Pretreatment......Page 1107
Obtaining Relations Without Non-observable Variables......Page 1108
Obtaining the Nodes of Each Cluster......Page 1109
Determination of the Context Network......Page 1110
Determination of the Minimal Diagnoses......Page 1112
Conclusions and Future Work......Page 1115
Introduction......Page 1117
Description and Dynamic CVT Model......Page 1118
Performance Criteria and Objective Functions......Page 1119
Constraint Functions......Page 1120
Optimization Problem......Page 1121
Algorithms......Page 1122
Discussion......Page 1124
Conclusions......Page 1125
Introduction......Page 1127
Methodology......Page 1128
Behavior-Based Architecture......Page 1129
Simulation Results......Page 1131
End-to-End Delay Results with TCP......Page 1132
Results with UDP......Page 1134
Conclusions......Page 1135
Introduction......Page 1137
Model Description......Page 1138
Testing Stability and Performance......Page 1142
Simulations and Results......Page 1143
References......Page 1146
Introduction......Page 1148
Initial Assumptions and Definitions......Page 1149
The VSC-Based Discrete-Time On-Line Learning Algorithm......Page 1151
Relation Between the Discrete-Time VSC-Based Learning of the Controller and the Quasi-Sliding Motion in the Behavior of the Controlled System......Page 1153
Quasi-Sliding Mode Control of Duffing Oscillator......Page 1154
Conclusion......Page 1155
References......Page 1156
Introduction......Page 1158
Wavelet Neural Networks......Page 1159
Robot System Model......Page 1160
Stability Analysis......Page 1161
Experimental Results......Page 1165
References......Page 1167
Introduction......Page 1169
Input-Output Data Modelling for Tilt Rotor Platform......Page 1171
Experimental Input-Output Data......Page 1172
Select Model Structure and Model Order Determination......Page 1173
Correlation Test......Page 1175
Experiment Validate Model......Page 1176
Conclusions......Page 1177
References......Page 1178
Introduction......Page 1179
Fuzzy Logic Control System......Page 1180
Simulation Results......Page 1184
References......Page 1187
Introduction......Page 1188
Background......Page 1189
Experiments......Page 1192
Results......Page 1193
Conclusions......Page 1197
Introduction......Page 1199
Architecture of an Affective ITS......Page 1200
Affective Student Model......Page 1201
Affective Behavior Model......Page 1203
Preliminary Results......Page 1204
Conclusions and Future Work......Page 1207
References......Page 1208
Introduction......Page 1209
Generic Architecture......Page 1210
Simulated Experiments......Page 1211
Exploration Characteristics......Page 1212
Relational Student Model......Page 1213
Evaluation Process......Page 1215
Conclusions and Future Work......Page 1217
References......Page 1218
Back matter......Page 1219