AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008,

front-matter.pdf......Page 1
Introduction......Page 15
Disparities on Road Surface......Page 21
Recalibration of Tilt Angle......Page 22
2D Motion on Road Surface......Page 23
Change in Depth for Image Features......Page 26
Evaluation......Page 28
Examples of Results......Page 29
References......Page 30
There Are Different Ways of Forgetting......Page 32
Motivation......Page 33
A Logic of Propositional Variable Forgetting......Page 35
Results, and Other Forgetting Operators......Page 38
Further Research......Page 41
References......Page 42
Introduction......Page 44
Bargaining Games......Page 45
Bargaining Solution......Page 47
Fixed Point Property......Page 50
Conclusion and Related Work......Page 53
References......Page 54
Introduction......Page 56
A Logic-Based Model for Analogy Making......Page 57
Coverage......Page 59
Algorithm......Page 60
References......Page 62
Introduction......Page 63
UCT and General Game Playing......Page 64
Structure of the Game Player......Page 65
Move Knowledge......Page 66
Experiments......Page 67
Conclusions and Future Work......Page 68
References......Page 69
Introduction......Page 70
Propositional Automata......Page 71
Cell Automata......Page 73
Comparison......Page 75
Conclusion......Page 79
References......Page 80
Introduction......Page 81
Related Work......Page 82
Topical Web Partitioning......Page 83
Probabilistic URI Filtering......Page 84
URI Clustering......Page 85
URI Cluster Selection......Page 87
Evaluations and Discussions......Page 88
Conclusions......Page 91
References......Page 92
Introduction......Page 93
Ontology Formalization in Conceptual Structure Theory......Page 94
Proposed New Ontology Formalism......Page 95
Properties of New Ontology Formalism......Page 96
References......Page 99
Exploiting Ontological Structure for Complex Preference Assembly......Page 100
Background......Page 101
Complex Preference Assembly Using Ontological Structure......Page 102
Analysis......Page 104
References......Page 106
Preliminaries......Page 107
OverallProcess......Page 108
Identifying NI by Discrimination Tree......Page 109
Identifying NI by Refutation......Page 110
Exploiting Table Constraints......Page 112
Implementation......Page 113
Related Work......Page 115
References......Page 116
Introduction......Page 118
Infeasibility Driven Evolutionary Algorithm (IDEA)......Page 120
CTP Problems......Page 122
Car Side Impact Problem......Page 123
Bulk Carrier Design Problem......Page 125
Trade-Off Studies......Page 126
Variation in Performance with Infeasibility Ratio......Page 127
References......Page 128
Introduction......Page 130
Subgraph Planning......Page 131
Constraint Programming......Page 132
Abstract Plan Steps......Page 133
Hall Ordering......Page 134
Experiment: The Patrick Port......Page 135
The Problem Domain......Page 136
Results......Page 137
Related Work......Page 139
References......Page 140
Introduction......Page 142
Petri Net Models for Markov Decision Processes......Page 143
PN Representations for Products of MDPs......Page 145
PN Unfoldings......Page 146
Multiple Components......Page 148
Conclusion......Page 150
References......Page 151
Introduction......Page 152
Partial-Order Task-Hierarchies......Page 153
The One-Room Problem......Page 154
Constructing Partial-Order Task-Hierarchies......Page 155
Multi-tasking Actions......Page 156
Mazes......Page 158
The Breakfast Task......Page 161
References......Page 162
Introduction......Page 164
Visibility Based Path Planning......Page 165
Distance Cost......Page 166
Path Planning Algorithm......Page 167
Experimental Results......Page 168
References......Page 170
Introduction......Page 171
Interpretation Process......Page 172
Estimating the Probability of an ICG......Page 173
Probabilistic Feature Comparison......Page 174
Colour......Page 175
Combining Feature Scores......Page 176
Evaluation......Page 177
Related Research......Page 179
References......Page 180
Introduction......Page 182
Simple Sentences......Page 183
Complex Sentences......Page 184
Anaphora Resolution......Page 186
Implementation......Page 187
Analysis of PENG Light Sentences......Page 188
Generation of PENG Light Sentences......Page 191
References......Page 192
Introduction......Page 194
Metrical Structure......Page 195
PCFG (Probabilistic Context-Free Grammar)......Page 196
Metrical PCFG Model......Page 197
Grammar Improving......Page 199
Expansion Operator......Page 200
Grammar Expansion for Large Corpus......Page 201
Conclusion......Page 204
References......Page 205
Introduction......Page 206
Experimental QA System......Page 207
Methods......Page 208
Conceptual Analysis......Page 209
Experimental Results and Discussion......Page 210
Lexical Coverage......Page 212
Concluding Remarks......Page 214
References......Page 215
Introduction......Page 216
Data and Training......Page 218
Iterative Relabeling......Page 220
Transforming Predicted Relevance Scores......Page 221
Estimating Recall......Page 223
Deployment......Page 224
References......Page 226
Computing Humor......Page 228
Modalin......Page 229
PUNDA Simple......Page 230
Third Person Evaluation......Page 232
User’s Evaluation......Page 233
Discussion......Page 234
The Method......Page 235
Results......Page 236
Solution to the Timing Problem......Page 237
References......Page 238
Introduction......Page 240
Related Work......Page 241
A Brief Introduction to TSVM......Page 242
Ensemble TSVM......Page 243
Experiment A......Page 244
Experiment B......Page 245
Experiment C......Page 246
Experiment D......Page 247
References......Page 248
Introduction......Page 250
Agreement Subtrees......Page 251
Size-of-Agreement-Subtree-Distribution Kernels......Page 253
The Recursive Formulas......Page 254
The Case of $\f x = \alpha^x$......Page 255
Evaluation of the Efficiency......Page 258
Experimental Results......Page 259
References......Page 260
Introduction......Page 261
Experimental Methodology from the Literature......Page 262
Problems Identified......Page 264
Simulation Study......Page 265
Discussion/Conclusions......Page 270
References......Page 271
Introduction......Page 272
Gaussian Processes......Page 273
GPO......Page 274
Double Pole Balancing with GPO......Page 276
Optimisation and Incremental Network Growth......Page 278
References......Page 280
Introduction......Page 282
Linear Discriminant Analysis......Page 283
Locality Sensitive Discriminant Analysis......Page 284
Neighborhood Preserving Embedding......Page 285
Experimental Design......Page 286
Results and Discussion......Page 288
References......Page 290
Introduction......Page 292
Hidden Markov Models......Page 293
Propositionalisation......Page 295
Experiments......Page 298
Conclusions and Future Work......Page 301
References......Page 302
Introduction and Biological Context......Page 303
Multiple Instance Learning......Page 305
Multiple Instance Learning for Promoter Prediction......Page 306
Materials and Methods......Page 307
PCPP and MIL on Positive-Classified Instances......Page 309
Conclusions and Further Work......Page 310
References......Page 311
Introduction......Page 314
MILES......Page 315
Experiment Design......Page 316
Experimental Results and Analysis......Page 317
Comparison of Base Learners for MILES......Page 318
Comparison of MILES to Other Wrapper Algorithms......Page 320
Overall Comparison of Classification Accuracy......Page 321
Conclusions......Page 322
References......Page 323
Introduction......Page 325
The Features of Major Hoeffding Tree Algorithm......Page 326
Tracking Ability to Concept Drift......Page 327
Accuracy Based Method......Page 328
Experimental Results......Page 329
References......Page 331
Introduction......Page 332
Bayesian LASSO Model Formulation......Page 333
Modeling Examples......Page 335
References......Page 337
Introduction......Page 339
Comparing Classifiers Fairly......Page 340
Evaluation Method......Page 344
Comparison on UCI Datasets......Page 345
Defining a Domain for One-Class Classification......Page 347
Conclusions......Page 349
References......Page 350
Introduction......Page 351
Definitions......Page 352
Decision Cluster Tree......Page 353
Model Selection and Classification......Page 354
Experiments......Page 355
Experiments on Synthetic Data......Page 356
Experiment on Real Data......Page 358
References......Page 360
Introduction......Page 362
Locality Graph for Spectral Clustering......Page 363
The Locality Spectral Clustering (LSC) Algorithm......Page 364
Results on the Corel Data Set......Page 365
References......Page 368
Introduction......Page 369
Using a Hoeffding Tree for Nearest Neighbour Search (HT-kNN)......Page 370
Using the Hoeffding Bound for Nearest Neighbour Search (HB-$k$NN)......Page 371
Datasets for Experiments......Page 372
Comparison of $k$NN with HB-$k$NN......Page 373
References......Page 375
Introduction......Page 376
Semi-supervised Learning......Page 377
Two-Stage Algorithm for Cross-Domain Text Classification......Page 378
Data Preparation......Page 379
Experimental Results......Page 381
Conclusions......Page 383
References......Page 384
Reviewing Existing Approaches to MORL......Page 386
MORL Benchmarks and Scalarisation Performance......Page 388
Conclusions and Future Work......Page 391
References......Page 392
Multiple Classification Ripple-Down Rules (MCRDR)......Page 393
Rated MCRDR......Page 394
Results......Page 395
References......Page 398
Introduction......Page 400
DHGN Algorithm Overview......Page 401
DHGN Bias Array Complexity......Page 402
Pattern-Level Recognition......Page 404
Results and Discussion......Page 405
References......Page 406
Introduction......Page 407
The Problem......Page 408
Definitions of Combined Patterns......Page 410
Interestingness Measures for Combined Patterns......Page 412
Redundancy in Combined Patterns......Page 413
A Case Study......Page 414
Conclusions......Page 416
References......Page 417
Introduction......Page 418
Preliminaries......Page 419
Related Work......Page 421
Construction......Page 422
Mining Process......Page 423
Experimental Results......Page 426
References......Page 428
Introduction......Page 430
Basic Definitions......Page 431
Topic Filtering Stage......Page 432
Pattern Mining Stage......Page 433
Conclusions......Page 435
References......Page 436
Introduction......Page 437
Enhancing Micro-aggregation with Dependence......Page 439
Data Sets......Page 441
Results......Page 442
References......Page 447
Introduction......Page 449
Regression Using Forward Stage-Wise Additive Modeling......Page 450
Ensembles of Weighted Simple Linear Regressors......Page 452
Modeling Multiple Targets Simultaneously with $k$-Means......Page 454
Related Work......Page 459
References......Page 460
Introduction......Page 461
Data Model: Correlation Graph......Page 462
Topical PageRank Based Algorithm......Page 463
A Simple Example......Page 464
Evaluation......Page 465
Experiment Results......Page 466
References......Page 467
Introduction......Page 468
Conceptual Clustering......Page 469
DynamicWEB......Page 470
Experiments......Page 472
Conclusion......Page 473
References......Page 474
Introduction......Page 475
Preliminaries......Page 477
Dynamic Updating Time-Evolving Microdata......Page 479
Experimental Results......Page 480
References......Page 482
Introduction......Page 484
Related Data Mining Techniques......Page 485
K-Nearest Neighbours Outlier Factor......Page 487
The Honeypot Data Analyser: hpdAnalyzer......Page 489
Experimental Results on Real-World Honeypot Data......Page 490
Conclusion and Discussion......Page 494
References......Page 495
Prudence Analysis (PA)......Page 496
Multiple Classification Ripple-Down Rules......Page 497
RM Applied to Prudence Analysis......Page 498
Experimental Method......Page 499
Versatility of RM......Page 500
References......Page 501
Introduction......Page 503
Introduction to XCS......Page 504
Framework for Clustering......Page 505
Population Initialization......Page 506
Adjust Rule Parameter......Page 507
Rule Merging......Page 508
Experimental Results......Page 509
References......Page 512
Introduction......Page 514
Poon-Domingos Models......Page 515
The Generalized-Jnt-Seg Model......Page 517
Experimental Results......Page 518
References......Page 520
Ship Design and Multi-agent System......Page 521
Case-Based Conflict Resolution in Agent-Based Preliminary Ship Design System......Page 523
Implementing Case Base and Conflict Resolution......Page 524
References......Page 527
back-matter.pdf......Page 528