This volume constitutes the refereed proceedings of the 4th International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2009, held in Salamanca, Spain, in June 2009. The 85 papers presented, were carefully reviewed and selected from 206 submissions. The topics covered are agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, real world HAIS applications and data uncertainty, hybrid artificial intelligence in bioinformatics, evolutionary multiobjective machine learning, hybrid reasoning and coordination methods on multi-agent systems, methods of classifiers fusion, knowledge extraction based on evolutionary learning, hybrid systems based on bioinspired algorithms and argumentation methods, hybrid evolutionry intelligence in financial engineering.
Author(s): Emilio Corchado, Xindong Wu, Erkki Oja, Alvaro Herrero, Bruno Baruque
Series: Lecture Notes in Computer Science - Lecture Notes Artificial Intelligence
Publisher: Springer
Year: 2009
Language: English
Pages: 735
Front matter......Page 1
Introduction......Page 21
Agent Types in HoCa......Page 22
Using HoCa to Development a Hybrid Multi-Agent System for Dependent Environment......Page 24
Results and Conclusions......Page 26
References......Page 27
Motivation and Related Work......Page 29
General Description......Page 30
The Inter-agents Communication Protocol......Page 31
Implementation and Evaluation......Page 33
Conclusions and Future Work......Page 36
References......Page 37
Introduction......Page 38
Hybrid and Deliberative Structure......Page 39
Reactive Structure......Page 41
Properties of Intention......Page 42
Demonstrations and Experiments......Page 43
Discussion and Conclusions......Page 44
References......Page 45
Introduction......Page 46
Multi-agent Based Personal File Management System......Page 47
Case Representation......Page 48
Case Retrieval......Page 49
Case Reuse and Revision......Page 50
Performance Validation......Page 51
Conclusions......Page 52
References......Page 53
Introduction......Page 54
Agent-Based Evolutionary System for Solving TSP......Page 55
Experimental Results......Page 57
References......Page 61
Introduction......Page 62
Evolutionary Algorithm: Memetic Algorithm......Page 64
The Vehicle Routing Problem. Public Transport Routes......Page 65
Solving the Problem Using a Memetic Algorithm......Page 66
Conclusions and Future Lines......Page 68
References......Page 69
Introduction......Page 70
Combining a Multi-agent Architecture and Case-Based Reasoning Systems......Page 71
Presenting a New Multi-agent Contingency Response System for Dynamic Environments......Page 72
Data Input Service......Page 74
Revision Service......Page 75
Results......Page 76
Conclusions and Future Work......Page 77
References......Page 78
Introduction......Page 80
Measuring Similarities between Customer Satisfaction Profiles......Page 81
Visualisation......Page 83
Results......Page 84
References......Page 87
Introduction......Page 88
An Overview of the System......Page 89
Development of a Decision-Maker......Page 90
Development of a Prototype of the Decision-Maker......Page 93
Experiments......Page 94
References......Page 95
Introduction......Page 97
Earthquakes and Data......Page 98
Wavelet Transformation......Page 99
Calculating Singularities......Page 100
Experimental Procedure......Page 101
Wenchuan Earthquake......Page 102
Puer Earthquake......Page 104
Conclusion......Page 106
References......Page 107
Introduction......Page 108
Evolutionary Based Automatic Design System......Page 109
Hydrodynamic Simulator......Page 110
Experiments......Page 112
Conclusions......Page 114
References......Page 115
Introduction......Page 116
The Automatic Design Procedure......Page 117
Hydrodynamic Simulation Model......Page 118
Neuronal Correction......Page 119
Design Example......Page 120
Conclusions......Page 122
References......Page 123
Introduction......Page 124
Itemsets, Supports of Itemsets, Frequent Itemsets, Downward Closed Sets......Page 125
Closures, Closed Itemsets......Page 126
Representing Patterns with Closures of Downward Closed Sets......Page 127
Summary and Conclusions......Page 130
References......Page 131
Introduction......Page 133
Transductive-Weighted Neuro-Fuzzy Inference System......Page 135
Tool Wear Model in Turning Process......Page 136
Results......Page 137
Discussion......Page 139
References......Page 140
Introduction......Page 141
Kalman Filters and Non-linear Variants......Page 142
Non-linear Enhancement of KF......Page 144
Information Fusion......Page 145
Conclusions......Page 146
References......Page 147
Introduction......Page 149
Coarse Candidate Selection Module......Page 150
Experimental Results......Page 151
Fine Module Performance......Page 152
HBHS Performance......Page 153
HBHS against Other Methods......Page 154
Conclusions and Future Work......Page 155
References......Page 156
Introduction......Page 157
Unsupervised Methods......Page 158
Derived Variables......Page 159
Results......Page 160
Conclusions......Page 163
References......Page 164
Introduction......Page 165
Format of the Generic Software Architecture......Page 167
Knowledge Representation – The Whiteboard......Page 169
Reasoning and Planning......Page 170
Modeling Behaviors......Page 171
Illustration and Conclusions......Page 173
References......Page 175
Introduction......Page 177
Connectionist Projection Model......Page 178
CBR Paradigm......Page 179
A Hybrid Advising Solution......Page 180
Proposal Generation......Page 181
A Real-Life Case Study......Page 183
Enhancing DIPKIP......Page 185
References......Page 187
Introduction......Page 189
Related Work......Page 190
Simplified Silhouette Filter (SSF)......Page 191
Empirical Evaluation......Page 192
Conclusions......Page 195
References......Page 196
Introduction......Page 197
The Multi-objective Variant of DE......Page 198
Search-Variable Representation and Description of the New algorithm......Page 199
Selecting the Objective Functions......Page 200
Evaluating the Clustering Quality......Page 201
Presentation of Results......Page 202
References......Page 204
Introduction......Page 207
ARES System......Page 208
Characteristics of Credibility Coefficient......Page 209
Ordinal Credibility Coefficient......Page 211
Multi Credibility Coefficient Method......Page 212
References......Page 214
Introduction......Page 215
EAC-I: Evolutionary Algorithm for Clustering Based Imputation......Page 216
Bias in Classification......Page 218
Illustrative Experimental Results......Page 220
References......Page 222
Motivation and Goals......Page 223
System Architecture......Page 224
Data Wrappers......Page 226
Meta ARM (Association Rule Mining) Scenario......Page 227
Classifier Generation Scenario......Page 228
Conclusions......Page 229
References......Page 230
Introduction......Page 231
Evolutionary Techniques......Page 232
Modules......Page 233
MDDSS......Page 234
Evolution of FS1 and Its Links......Page 235
Conclusions and Expected Outcomes......Page 237
References......Page 238
Introduction......Page 239
Capability Maturity Model Integrated (CMMI)......Page 240
Fuzzy Set Theory......Page 241
Algorithm of FQIMM......Page 242
Conclusions......Page 245
References......Page 246
Introduction......Page 247
Related Work......Page 248
C2D: Confidence-Based Concept Discovery Method......Page 249
Transitive Rules in C2D......Page 250
Experimental Results......Page 252
Conclusion......Page 253
References......Page 254
Introduction......Page 255
Business Intelligence and Energy Markets Related Work......Page 256
Prediction......Page 257
Modeling......Page 258
Conclusions and Future Work......Page 259
References......Page 260
Introduction......Page 264
LIRBF Model......Page 266
Learning the LIRBF Model Coefficients......Page 268
Experiments......Page 269
References......Page 271
Introduction......Page 272
ELD Problem Formulation......Page 273
The Hybrid Algorithm......Page 275
Six-Unit System......Page 277
Thirteen-Unit System......Page 278
References......Page 279
Introduction......Page 281
The Density Classification Task and Related Work......Page 282
The Space Dimension......Page 283
Circular Evolutionary Approach to the Density Classification Task......Page 284
Numerical Experiments......Page 285
Conclusions and Future Work......Page 287
References......Page 288
Introduction......Page 289
University Course Timetabling......Page 290
The Hybrid Strategy......Page 291
Initialisation Heuristic......Page 292
Non-linear Great Deluge......Page 293
Experiments and Results......Page 294
References......Page 295
Introduction......Page 297
Agent-Based Co-operative Co-evolutionary System for Multi-objective Optimization......Page 298
The Experiments......Page 301
Summary and Conclusions......Page 303
References......Page 304
Introduction......Page 305
Scheduling of Independent Tasks in Computational Grids......Page 307
The Proposed GA(TS) Hybrid Approach......Page 308
Tabu Search for the Scheduling Problem in Grids......Page 309
Experimental Study......Page 310
Conclusions......Page 311
Introduction......Page 313
Background......Page 314
Understanding Model–Building......Page 315
Improved Fitness Assignment......Page 317
Competent Model Builders......Page 318
References......Page 319
Introduction......Page 321
Background......Page 322
Local Search......Page 324
HJMA......Page 325
Varying the Number of Peaks......Page 326
References......Page 327
Introduction......Page 330
Evolutionary Programming......Page 332
Proposed MDE Algorithm: A Hybridized Version of DE and EP......Page 333
Numerical Results......Page 334
Conclusions......Page 336
References......Page 337
Introduction......Page 339
Oscillatory Clusters and Input Dataset......Page 340
CNN Modeling......Page 342
Proposed Method to Detect Fragmentary Synchronization......Page 344
References......Page 346
Introduction......Page 347
The Ensemble of Neural Predictors......Page 348
Final Predictor......Page 350
Numerical Results......Page 351
References......Page 354
Introduction......Page 356
TERSQ Algorithm......Page 357
Hybrid Evolutionary Algorithm......Page 359
Videogame Characters......Page 360
Conclusions......Page 362
References......Page 363
Introduction......Page 364
GTM and Geodesic Metric......Page 365
Geo-GTM......Page 366
A Semi-supervised Extension of Geo-GTM......Page 367
Experimental Results and Discussion......Page 368
References......Page 371
Introduction......Page 372
The Hot Strip Mill Process......Page 373
IT2 Design......Page 374
Experimental Results......Page 377
References......Page 378
Introduction......Page 380
Design Engineering......Page 381
Process Planning......Page 382
The Assembly Line Balancing Problem......Page 383
The Dynamic Scheduling Problem......Page 384
References......Page 385
Introduction......Page 388
Signal Data Analysis......Page 389
Confidence Estimation......Page 390
Network Output Analysis......Page 391
Expert Pooling......Page 393
References......Page 395
Introduction......Page 396
The Proposed Hybrid Ant-Based Model......Page 397
Solution Construction and Pheromone Update......Page 398
Insertion-Based Local Search......Page 399
Numerical Results......Page 400
Conclusions and Future Work......Page 402
References......Page 403
Motivation......Page 404
The Design Stage......Page 405
Definition of the Thermal Dynamics......Page 406
Experiments and Commented Results......Page 407
Conclusions and Future work......Page 409
References......Page 410
Introduction......Page 411
Self-Organizing Maps (SOM)......Page 412
Fuzzy Interpretation of the Topographical Map......Page 413
Biomedical Signals - Patients with Diabetes......Page 415
Sampling of Data from Diabetes......Page 416
Implementations and Results......Page 417
References......Page 419
Unearth the Hidden Supportive Information for an Intelligent Medical Diagnostic System......Page 421
Nature of Medical Data......Page 422
MIDCA......Page 423
Intelligence of MIDCA......Page 424
LSIA Discovery......Page 425
Experimental Results......Page 426
References......Page 427
Introduction......Page 429
Incremental Kernel Machines......Page 430
The Spectrum Kernel......Page 431
Experiments......Page 432
Results......Page 433
References......Page 435
Introduction......Page 437
Noise......Page 438
Data Sets......Page 439
Results......Page 440
Conclusion......Page 443
References......Page 444
Introduction......Page 445
Voxel Based Descriptor......Page 447
Supervised Growing Neural Gas (SGNG)......Page 448
Conclusion......Page 451
Introduction......Page 453
Accuracy Versus Sensitivity......Page 454
Base Classifier Framework and Objective Functions......Page 455
MPDE Algorithm......Page 456
Experiments......Page 458
Conclusions......Page 460
References......Page 461
Introduction......Page 462
Related Work......Page 463
Core Algorithm......Page 464
Langley Glide-Back Booster (LGBB)......Page 465
Results......Page 466
Summary......Page 468
References......Page 469
Introduction......Page 470
Using Multi-objective G3P for Classification Rule Generation......Page 471
Genetic Operators......Page 472
Fitness Function......Page 473
Comparison of Multi-objective Strategies......Page 474
References......Page 477
Introduction......Page 479
Formalization of the ASBO Argumentation System......Page 480
The Interaction Protocol......Page 481
The Context and the State of a Conversation......Page 482
Effect Rules......Page 483
Termination and Outcome Conditions......Page 484
An Example......Page 485
Conclusions......Page 486
Motivation......Page 488
Social Structure......Page 489
Protocol Specification......Page 491
Normative Context Definition......Page 492
Application Example......Page 493
Conclusion......Page 494
References......Page 495
Introduction......Page 496
CBR as Deliberative Mechanism for Agents......Page 497
Temporal-Bounded CBR......Page 499
Revise Phase......Page 500
Reuse Phase......Page 501
References......Page 502
Introduction......Page 504
Event Tracing in Multiagent Systems......Page 505
Functional Requirements......Page 507
Efficiency Requirements......Page 508
Security Requirements......Page 509
Conclusions and Future Work......Page 510
Introduction......Page 512
Web Service Security Problem Description......Page 513
Classifier Agent Internal Structure......Page 514
Mechanism for the Classification of SOAP Message Attack......Page 515
References......Page 518
Introduction......Page 520
The AgUser’s Circulation and the Dynamics of Belief Change......Page 521
RecMAS: Prototype and Implementation......Page 523
AgentComs: Trading Knowledge through Links......Page 524
Complex Optimization Problem......Page 525
Optimization Using Wireless Communications......Page 526
Final Results: Strategies for the Adaptation in Real Time and the Dynamic Attraction of Clients......Page 527
Conclusions and Future Work......Page 528
References......Page 529
Introduction......Page 530
Classifier Fusion......Page 531
Dudani's Weighting Function and the Inverse Distance Weight......Page 532
Experiments and Results......Page 533
Concluding Remarks......Page 535
Introduction......Page 537
Machine Learning Ensembles......Page 538
Ensemble of Vector Quantization Neural Networks Using Fusion......Page 539
Networks Fusion Using Boosting......Page 540
Experimental Results......Page 541
Conclusions and Further Works......Page 543
Introduction and Related Works......Page 545
Model of Compound Classifier......Page 546
Learning AdaSS Algorithm......Page 547
Experimental Investigation......Page 549
Conclusions......Page 551
References......Page 552
Bayes Hierarchical Classifier......Page 553
Decision Problem Statement......Page 554
Basic Notions of Intuitionistic Fuzzy Events......Page 556
Global Optimal Strategy......Page 557
Illustrative Example......Page 558
Conclusion......Page 559
References......Page 560
Introduction......Page 561
Classifier Fusion Based on Classifier Response......Page 562
Classifier Fusion Based on Values of Classifiers’ Discrimination Function......Page 563
Example of Classifier Fusion Based on Weights Depended on Classifier and Class Number......Page 564
Experimental Investigation......Page 566
Final Remarks......Page 567
References......Page 568
Introduction......Page 569
Bumble Bees Behavior......Page 570
BBMO for the Feature Selection Problem......Page 571
GRASP for the Clustering Problem......Page 572
Computational Results......Page 573
Conclusions and Future Research......Page 575
Introduction......Page 577
Evolutionary Instance and Feature Selection......Page 578
Cooperative Coevolution......Page 579
Cooperative Coevolutive Model Based on Instance and Feature Selection Using CHC......Page 580
Results......Page 582
Concluding Remarks......Page 583
Introduction......Page 585
Uncertainty and Feature Selection in Unsupervised Problems......Page 586
The Fuzzy Unsupervised Mutual Information Feature Selection Algorithm......Page 587
The Unsupervised Algorithm......Page 588
Experiments and Results......Page 589
Conclusions and Future Works......Page 590
References......Page 591
Introduction......Page 593
Subgroup Discovery......Page 594
NMEF-SD: Non-dominated Multi-objective Evolutionary Algorithm Based on the Extraction of Fuzzy Rules for Subgroup Discovery......Page 595
Re-initialization Based on Coverage......Page 597
Experimentation......Page 598
Conclusions......Page 599
Introduction......Page 601
Imbalanced Data-Sets in Classification......Page 602
IVFSs Model......Page 603
Genetic Tuning of the Amplitude of Upper Bound of the IVFS......Page 604
Experimental Set-Up......Page 605
Conclusions......Page 607
Introduction......Page 609
FS and FC in Presence of Attribute Interaction......Page 610
Data Reduction Using MFE3/GA......Page 611
Empirical Study......Page 613
Conclusion......Page 615
Introduction......Page 617
Related Work......Page 618
Analia......Page 619
Multiobjective Clustering in Analia......Page 620
Experiments......Page 621
Conclusions......Page 623
Introduction......Page 625
Data Complexity......Page 626
Why?......Page 627
How?......Page 628
Process Organization and Genetic Operators......Page 629
Experimental Results......Page 630
Conclusions......Page 632
Introduction......Page 633
Mamdani Fuzzy Rule-Based Systems......Page 634
MF Parameter Learning......Page 635
Interpretability......Page 636
The Three-Objective Evolutionary Approach......Page 637
Experimental Results......Page 638
References......Page 640
Introduction......Page 641
Proposed Algorithm Description......Page 642
Case Study......Page 644
Solution Obtained by the Proposed Algorithm......Page 645
Comparative Analysis of the Obtained Solutions by Other Approaches......Page 647
Conclusions and Future Work......Page 648
Introduction......Page 649
Individual Representation......Page 650
Genetic Operators......Page 651
Evolutionary Algorithm......Page 652
Implementation......Page 654
Results and Discussion......Page 655
Conclusions and Future Work......Page 656
Introduction......Page 658
Genetic Learning......Page 659
Proposed Method......Page 660
Local Nodes......Page 661
Experimental Study......Page 662
Results Analysis......Page 663
Conclusions and Future Work......Page 664
Introduction......Page 666
Preliminaries: Genetic Extraction of Association Rules......Page 667
Association Rules Mining through Evolutionary Algorithms: EARMGA, GAR, and GENAR......Page 668
Experimental Results......Page 670
References......Page 672
Introduction......Page 674
Minimum Risk Genetic Fuzzy Classifiers with Crisp Data......Page 675
Minimum Risk Genetic Fuzzy Classifiers with Low Quality Data......Page 676
Computer Algorithm of the Generalized GFS......Page 677
Synthetic Dataset......Page 678
Concluding Remarks and Future Work......Page 680
Introduction......Page 682
Experiment Strategy: Factorial Design......Page 683
Algorithm Neighboring-Ant Search......Page 684
Characteristics of SQRP......Page 685
Performing the Experiment......Page 686
Analyzing Statistics Results......Page 687
Conclusions and Future Works......Page 688
References......Page 689
Introduction......Page 690
Vehicle Routing Problem (VRP)......Page 691
Ant Colony System Optimization: State of Art......Page 692
Distributed Q-Learning: A Learning-Based Approach......Page 694
Experimentation and Results......Page 695
References......Page 696
Grid Creation and Weighting......Page 698
Creating Preliminary Clusters......Page 699
The Data Table......Page 700
Small Dataset Example......Page 701
Average Error......Page 702
Large Dataset Test......Page 703
Conclusions......Page 704
References......Page 705
Introduction......Page 706
Logistic Function and Adapted Logistic Curve......Page 707
Results and Measurements......Page 708
Available Analogue Implementation......Page 712
References......Page 713
Introduction......Page 714
Particle Swarm Optimisation......Page 715
Forecasting Models......Page 716
Trading-Related Objective Functions......Page 717
Empirical Study......Page 718
Discussion-Further Research......Page 720
Introduction......Page 722
Literature Review......Page 724
Hybrid Ant Colony Optimization Algorithm with a Local Search Technique......Page 726
Computational Study......Page 728
Conclusions and Further Research......Page 731
References......Page 732
Back matter......Page 733
