Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City,

Front matter......Page 1
Introduction......Page 12
A Hybrid Transfer of Control Model......Page 13
Strategy Generation......Page 15
Strategy Evaluation......Page 16
Examples......Page 18
Discussion and Related Work......Page 21
Conclusions......Page 23
Introduction......Page 24
Coalition in Linear Production Domains......Page 26
Best Coalition and Coalition Structure Pattern......Page 27
Deliberating Process......Page 28
Generating Coalition Structure......Page 30
Example......Page 31
Related Work......Page 33
Conclusion and Future Work......Page 34
Introduction......Page 36
Recognition Space Model......Page 37
Variable Plan......Page 38
Plans Composition......Page 39
Disunification for Recognition Space Lattices......Page 40
Recognition of Activities in a Smart Home......Page 42
High-Level Recognition Service......Page 43
Related Works......Page 45
Conclusion......Page 46
Introduction......Page 48
Constraints of Web and Mobile Map Generation......Page 49
Knowledge-Based Approaches for On-the-Fly Map Generation......Page 50
Use of Multiagent Systems for On-the-Fly Map Generation......Page 51
Agents’ Interactions......Page 52
Architecture of Our Multiagent System......Page 54
Application: The SIGERT System......Page 55
Conclusion......Page 58
References......Page 59
Introduction......Page 60
Multiagent Task Associated Markov Decision Process (MTAMDP)......Page 61
The MTAMDP Functions......Page 64
Value Iteration for MTAMDPs......Page 65
Acyclic Decomposition Algorithm......Page 67
Discussion and Experimentations......Page 68
Conclusion and Future Work......Page 70
Introduction......Page 72
Satisfaction Equilibrium......Page 73
Satisfaction Function and Equilibrium......Page 74
Satisfying Strategies and Other Problematic Games......Page 75
Mixed Satisfaction Equilibrium......Page 76
Pure Satisfaction Equilibrium with Fixed Constants......Page 77
Empirical Results with the PSEL Algorithm......Page 78
Convergence of the PSEL Algorithm......Page 79
Limited History Satisfaction Learning (LHSL) Algorithm......Page 80
Empirical Results with the LHSL Algorithm......Page 81
Conclusion and Future Works......Page 83
Introduction......Page 84
Previous Research Works......Page 85
Requirements for ‘Believable’ Simulation......Page 86
The MAGS: The MultiAgent GeoSimulation Platform......Page 87
The Mall-MAGS Prototype: A Multiagent-Based Simulator of the Shopping Behavior in a Mall......Page 89
A ‘Usable’ Agent-Based Geosimulation Prototype: The Case of the Square One Shopping Mall (Toronto)......Page 91
The Use of the Mall_MAGS Prototype......Page 92
Conclusion and Future Works......Page 94
References......Page 95
Introduction......Page 97
Minimum-Square-Error Profile Alignment......Page 99
Optimizing the Number of Clusters......Page 100
Experimental Results......Page 102
Conclusions......Page 107
Introduction......Page 109
Macroscopic and Volumetric Modeling......Page 110
Glioma Modeling Based on White Matter Invasion......Page 111
Diffusion Models......Page 112
Experiments......Page 114
Feature Selection......Page 115
Model Performance Versus Tumor Grade......Page 117
Statistical Evaluation of the Three Models......Page 118
Contributions and Future Work......Page 119
Introduction......Page 121
Data Specification......Page 123
Learning by Bayesian Inference......Page 124
Developing Environment......Page 127
Results and Discussion......Page 128
Conclusion......Page 130
References......Page 131
Introduction......Page 133
Constraint Relaxation with Two Semirings......Page 134
Example Relaxation Problem......Page 136
Relaxing Other Solutions......Page 137
Constraint Relaxation with One Semiring......Page 138
Example......Page 141
Applications......Page 143
Introduction......Page 145
Specification of an IP Problem......Page 146
Information Personalization Requirements......Page 147
Defining IP as a Constraint Satisfaction Problem......Page 148
Constraint Satisfaction Based IP Framework......Page 149
Our Approach for Consistency Constraint Acquisition......Page 150
Solving the Constraint Satisfaction Problem for Information Personalization......Page 151
Evaluations of Variants of Partial Forward Checking......Page 154
Concluding Remarks and Future Work......Page 155
References......Page 156
Introduction......Page 157
Preliminaries......Page 159
The Quality of Random Decisions......Page 160
Quantity of Randomness......Page 164
Conclusions......Page 167
Introduction......Page 170
Modelling Arguments......Page 171
Solving with Arguments......Page 173
Results......Page 176
Comparisons......Page 179
Conclusion......Page 180
Introduction......Page 182
Background......Page 183
Noisy-AND Gates for Reinforcement and Undermining......Page 184
Noisy-AND Trees......Page 185
Noisy-AND Tree Evaluation......Page 187
Relaxing Default Assumptions......Page 188
Elicitation of CPTs with Noisy-AND Trees......Page 189
Related Models of Causal Interaction......Page 190
Conclusions......Page 193
Introduction......Page 194
Bayesian Networks......Page 195
Probabilistic Inference......Page 197
Arc Reversal......Page 198
New Approach LAZY-ARVE for BN Inference......Page 199
Related Works......Page 201
Conclusions......Page 204
Introduction......Page 206
Default Logic......Page 207
Four-Valued Logic......Page 208
Four-Valued Default Logic......Page 209
Transformation of Formulae......Page 212
Relation Between Models and Extensions......Page 213
Related Work......Page 215
Conclusion......Page 216
Introduction......Page 217
Elimination Trees and Conditioning Graphs......Page 218
Relevant Variables......Page 221
Results......Page 225
Conclusions and Future Work......Page 227
Introduction......Page 229
Graphical Models......Page 230
Melodic Representation......Page 231
Modeling Root Note Progressions......Page 232
Chord Model......Page 235
Chord Model Given Root Note Progression and Melody......Page 236
Conclusion......Page 239
d-Separation, Stochastic Independence, and the Faithfulness Assumption......Page 241
An Example Where the IC Algorithm Fails......Page 243
Failure of Faithfulness Due to Deterministic Relations......Page 244
Statistical Indistinguishability Imposed by Determinism......Page 246
A Sufficient Condition for Identifiability......Page 247
Detecting Deterministic Relations......Page 248
Experimental Results......Page 249
Discussion and Open Problems......Page 251
Introduction......Page 253
Abstract Argumentation Framework......Page 255
Argumentation Semantics......Page 258
Progressive Defeat Paths......Page 260
Conclusions......Page 263
Subject Agreement......Page 265
Addressee Agreement......Page 266
Lexicon and Subject Agreement......Page 267
Object and Oblique Agreement......Page 269
Multiple Honorification......Page 270
Agreement in Auxiliary Constructions......Page 271
Addressee Agreement......Page 274
Testing the Feasibility of the Analysis......Page 275
Conclusion......Page 276
Introduction......Page 277
Generating Gazetteers......Page 278
Resolving Ambiguity......Page 280
Evaluation with the MUC-7 Enamex Corpus......Page 282
Evaluation with Car Brands......Page 285
Supervised Versus Unsupervised......Page 286
References......Page 287
Introduction......Page 289
Related Work......Page 290
Cluster Labeling......Page 292
The Algorithm......Page 293
Experimental Setup and Results......Page 294
The Test Set Results......Page 295
Conclusion......Page 297
Introduction......Page 299
Negotiation Strategies and Communication......Page 300
Language and Strategies......Page 301
Building Language Patterns for Influence......Page 302
Extraction of Language Patterns from E-Negotiation Texts......Page 304
Early Classification of the Negotiation Outcomes......Page 306
Conclusions and Future Work......Page 308
References......Page 309
The Problem......Page 311
A Comparison......Page 312
Proposed Solution......Page 313
Indexing......Page 314
Query Rewriting and Reformulation......Page 316
Evaluation......Page 318
Quantitative Evaluation......Page 319
Conclusion and Future Work......Page 321
Objectives......Page 323
Potential for Dependency Analyses......Page 324
Filtering......Page 326
Anti-filters......Page 327
Evaluation......Page 328
Results......Page 329
Compression......Page 330
Some Problems with the Grammar or the Corpus......Page 331
Possible Improvements......Page 332
Conclusion and Future Work......Page 333
Introduction......Page 335
Related Work......Page 336
Our Approach and Dataset......Page 337
Syntactic Representation and Experiments......Page 338
Relational Representation and Experiments......Page 341
Conclusions and Future Work......Page 343
References......Page 344
Introduction......Page 347
Sentiment Tag Extraction from WordNet Glosses......Page 349
NP-Based Filtering: Senti-Sense System......Page 350
Results and Evaluation......Page 353
Conclusions and Future Work......Page 355
Introduction......Page 358
Complications in Practical Fraud Detection Research......Page 359
Benford's Law......Page 360
Reinforcement Learning......Page 361
Algorithm......Page 362
Experiments......Page 365
Conclusions......Page 368
Introduction......Page 370
Formal Model and Algorithms......Page 371
Problem Description......Page 372
Partial Observability......Page 373
FriendQ with a Partial Local View......Page 374
Results......Page 375
Related Work......Page 379
Conclusion......Page 380
Introduction......Page 382
Defining a Game Using Traces......Page 383
Constructing the MDP M......Page 385
Value of a Policy in M......Page 387
Theorems and Definition of divtrace( ."026B30D .)......Page 388
Implementation and PAC Guarantees......Page 390
Experimental Results......Page 391
Conclusion......Page 392
Introduction......Page 394
Background on Partially Observable Markov Decision Processes......Page 395
Belief Point Selection......Page 397
Selecting Belief States Based on Reachability......Page 398
Value-Based Selection......Page 402
Empirical Evaluation......Page 403
References......Page 405
Introduction......Page 406
Related Work......Page 407
Hierarchical Categorization Task......Page 408
Hierarchical Global Learning Algorithm......Page 409
Hierarchical Evaluation Measure......Page 411
Datasets......Page 414
Comparison with Hierarchical Local Approach......Page 415
Conclusion......Page 416
Introduction......Page 418
Theoretical Model......Page 419
The $Core Based Adaptive k-Means$ Algorithm......Page 421
The $Hierarchical Core Based Adaptive$ Algorithm......Page 422
Quality Measures......Page 423
$CBAk$ Results......Page 425
Adaptive Horizontal Fragmentation in Object Oriented Databases......Page 427
Conclusions and Future Work......Page 428
Introduction......Page 430
Information Tables and a Decision Logic Language......Page 431
Formal Concept Analysis......Page 432
Logical Concept Analysis Limited to Conjunction......Page 433
Classification Rules......Page 434
Consistent Classification Rules and Consistent Concepts......Page 435
An Algorithm for Finding the Most General Consistent Concepts......Page 437
Experiments......Page 439
Conclusion......Page 440
Introduction......Page 442
Review of Quantum Information Processing Concepts......Page 443
Previous Encounters of Machine Learning with Quantum Information Processing......Page 444
Training with a Quantum Dataset......Page 445
Possible Learning Strategies......Page 446
Hierarchy of Quantum Learning Classes......Page 447
Measure of Distance Between Quantum States......Page 448
Examples of Possible Quantum Clustering Algorithms......Page 449
Experimentation......Page 450
Conclusions and Open Problems......Page 452
Introduction......Page 454
Gait Signature Extraction......Page 455
Pattern Classification......Page 457
Gait Cycle Estimation......Page 458
CMU Database......Page 460
NLPR Database......Page 462
KTU Database......Page 463
Conclusion......Page 464
Introduction......Page 466
Related Work in Decision Tree Probability Estimation......Page 468
The Performance Evaluation Metrics......Page 469
A New Algorithm for Learning CLLTree......Page 470
Experimental Methodology and Results......Page 472
Conclusion......Page 477
Introduction......Page 478
Linear Dimensionality Reduction Schemes......Page 479
Performance on Synthetic Data......Page 482
Performance on Real-Life Data......Page 485
Conclusions......Page 488
Introduction......Page 490
Discriminative vs. Generative Classifiers......Page 491
Cost Curves......Page 492
Decision Trees......Page 494
Support Vector Machines......Page 496
Neural Networks......Page 498
Conclusions......Page 500
Introduction......Page 502
Problem Formulation......Page 503
Known Uses......Page 504
Enumerating the K Best Paths......Page 505
Target Utilities......Page 506
Choosing a Good $K$......Page 507
Experimental Results......Page 508
Constructing the Training Set......Page 509
Results......Page 510
Discussion and Future Work......Page 511
Introduction......Page 514
Related Work......Page 516
Feature Selection for Probability Estimation of Naive Bayes......Page 518
Conclusions and Future Work......Page 523
Introduction......Page 526
Related Work......Page 528
Lazy Averaged One-Dependence Estimators......Page 530
Experimental Methodology and Results......Page 532
Conclusions......Page 535
Introduction......Page 537
Related Work......Page 539
An Example......Page 540
Performing Classification and Ranking Based on Probability......Page 541
Experiments......Page 544
Conclusions......Page 547
Introduction......Page 549
Background......Page 550
Related Work......Page 551
Motivation......Page 552
One-Class Classification Framework......Page 553
Parameter Search Algorithms......Page 554
Evaluation Measurement......Page 556
Results......Page 557
Conclusion......Page 559
Introduction......Page 561
Background......Page 563
Mixed Initiative System......Page 564
Sudoku Strategy Graphs......Page 567
Sudoku Skill Matrix......Page 568
Initializing the Student Model......Page 569
A Sample Session......Page 570
Conclusions and Further Research......Page 571
References......Page 572
Back matter......Page 573