Case-Based Reasoning Research and Development: 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast Northern Ireland, UK, August

Front matter......Page 1
Introduction......Page 12
Programming Soccer Robots......Page 13
Perception......Page 14
Act......Page 15
Decision Making......Page 16
What Are Cases, and Where Do They Come from?......Page 17
CBR in RoboCup -- An Applicational View......Page 19
CBR Methods for Opponent Modeling......Page 20
CBR Methods for Situation Analysis and Decision Making......Page 21
CBR Methods for Coaching......Page 23
Conclusion......Page 24
Introduction......Page 27
The Knowledge Sifter Agent-Based Architecture......Page 28
Emergent Semantics in Knowledge Sifter......Page 31
Case-Based Knowledge Sifter Framework......Page 32
Semantic Case Representation......Page 33
Case Retrieval Via Ontology-Based Indices......Page 34
Collaborative Incremental Query Specification......Page 36
Conclusions......Page 40
Introduction......Page 42
Lazy Induction of Descriptions......Page 43
Lazy Generalizations for Building Lazy Domain Models......Page 44
Generalizations and Explanations......Page 45
A Case Study: Predictive Toxicology......Page 47
Representation of Chemical Compounds......Page 48
Assessing Carcinogenic Activity to Chemical Compounds......Page 50
The Explanation Scheme......Page 52
Conclusions......Page 54
Introduction......Page 57
Related Work......Page 59
Case Definition......Page 60
Case Retrieval......Page 62
Multi-robot Architecture and Case Execution......Page 65
Evaluation......Page 66
Behavior-Based Approach......Page 67
Case-Based Approach......Page 68
Conclusions and Future Work......Page 71
Introduction......Page 72
Case Retrieval Networks......Page 74
Higher Order Associations......Page 75
An Example......Page 77
Modeling Word Similarities......Page 78
Experimental Methodology......Page 79
Analysis of Results......Page 80
Incorporating Class Knowledge into Word Similarities......Page 82
Learning Model Parameters Automatically......Page 83
Discussion......Page 84
Related Works......Page 85
References......Page 86
Introduction......Page 88
Label Ranking and CBR......Page 89
Training Data in Label Ranking......Page 90
Prediction and Loss Functions on Label Rankings......Page 91
Case-Based Label Ranking......Page 92
Aggregating Label Rankings......Page 93
Extensions of Label Ranking......Page 94
Experiments......Page 95
Case-Based Decision Making......Page 96
Label Ranking for Controlling Heuristic Search......Page 97
Summary and Conclusions......Page 100
Introduction......Page 103
Related Work in Noise Reduction......Page 104
Profiling to Identify Harmful Cases......Page 105
Assessing Confidence......Page 106
Profile Approach......Page 107
Complexity-Guided Error Reduction......Page 108
Setting the Threshold Level......Page 109
Threshold Error Reduction Algorithm......Page 110
Datasets......Page 111
Initial Experiments......Page 112
Experiments on Datasets with Artificial Noise......Page 114
Conclusions......Page 116
Introduction......Page 118
Overview of Progressive Critiquing......Page 119
Analysis of Progressive Critiquing......Page 122
Mixed-Initiative Relaxation of Constraints......Page 125
Empirical Study......Page 128
Conclusions......Page 131
References......Page 132
Motivation......Page 133
Related Work......Page 134
Description of the Methodology......Page 135
The Strategy Map......Page 136
Evaluation of the Case Retrieval Strategies......Page 139
Experiments, Results, and Discussion......Page 141
Assessing the Performance of the Case Retrieval Strategies......Page 142
Conclusions and Further Research......Page 145
Introduction......Page 148
Typed Sequences Overview......Page 149
Computing Quality of Cases......Page 151
Step Utility Measure......Page 152
Sequence Utility Measure......Page 153
Study on Training Problems......Page 154
Test Using the Utility Measure......Page 155
Related Work......Page 157
Conclusions and Future Work......Page 158
Introduction......Page 160
SOM and ViSOM......Page 162
How to Find the Target Case Solution......Page 165
Scenario Representation......Page 167
Data Collection......Page 170
Evaluation......Page 171
References......Page 173
Introduction......Page 175
Related Work......Page 176
Case-Based Planning in WARGUS......Page 178
A Behavior Reasoning Language......Page 179
Behavior Acquisition in WARGUS......Page 181
Real-Time Plan Expansion and Execution......Page 183
Behavior Generation......Page 184
Experimental Results......Page 187
Conclusions......Page 188
Introduction......Page 190
Related Work......Page 191
The SmartHouse Domain......Page 192
Term Extraction from Textual Reports......Page 193
Latent Semantic Indexing......Page 194
Term Filtering......Page 195
Formal Concept Analysis......Page 197
FCA Objects and Attributes......Page 198
Case Representation and Organisation......Page 199
Evaluation......Page 201
Conclusions and Future Work......Page 203
Introduction......Page 205
The Fallacy of Feedback......Page 206
Motivations for Studying Case Provenance......Page 207
Experimental Design and Results......Page 209
Test 1: Solution Quality with Delayed Feedback......Page 210
Test 3: Using Feedback Propagation to Improve Case Base Quality......Page 212
Test 5: Targeted Feedback......Page 215
General Observations......Page 216
Related Work......Page 217
Conclusion......Page 218
Introduction......Page 220
Framing the Problem......Page 221
The WebAdapt System......Page 222
Extracting Role-Filler Constraints......Page 223
Finding Replacement Elements That Satisfy Multiple Constraints......Page 225
Generality of the Strategies......Page 226
Evaluation......Page 227
Related Work......Page 231
Future Issues and Outlook......Page 232
Introduction......Page 235
Agile Workflows......Page 236
Representation and Retrieval of Workflow Instances......Page 238
Similarity Assessment and Index-Based Retrieval......Page 239
Similarity Measure for Restricted Workflows......Page 241
Similarity Measure for Workflows with Control Flow Elements......Page 243
Formative Evaluation......Page 245
Conclusion......Page 247
Introduction......Page 250
Overview of the Paper......Page 251
Principle of Conservative Adaptation......Page 252
Katsuno and Mendelzon's Axioms......Page 253
Conservative Adaptation Process Based on a Revision Operator......Page 254
Revision Axioms and Conservative Adaptation......Page 255
The KASIMIR Project......Page 256
Examples......Page 257
Discussion......Page 259
Conclusion and Future Work......Page 261
Introduction......Page 265
A CBR System for Quotation Processing......Page 266
Data Quality and Data Quality Management......Page 267
Phases of Operational Data Quality Management......Page 268
The Goal-Question-Metrics-Approach......Page 269
Deriving Data Quality Measures......Page 270
Processes for Measuring and Evaluating Data Quality......Page 273
Closed-Loop Control as a Process Framework......Page 274
Integrating Control Loops in the Case-Based Reasoning Cycle......Page 275
Conclusion......Page 277
Introduction......Page 280
Previous Work......Page 281
Case-Based IDS Architecture......Page 283
Case Representation......Page 284
Dissimilarity Metric......Page 287
Experimental Results......Page 289
Conclusions......Page 291
Introduction......Page 295
Mapping the World......Page 296
Case-Based Architecture......Page 298
Signal Representation......Page 301
Similarity Metric......Page 302
Experimental Results......Page 303
Conclusions......Page 308
Introduction......Page 310
Related Work......Page 311
Recommendation Generation......Page 312
Explanation and Consensus......Page 313
Critique-Based Profiling and Recommendation......Page 314
Generating Group Recommendations......Page 315
Methodology......Page 317
Results: The Individual's Perspective......Page 319
Results Summary......Page 321
Conclusions......Page 322
Introduction......Page 325
Email Classification Using Examples (ECUE)......Page 326
The Feature-Based Distance Measure......Page 327
The Feature-Free Distance Measure......Page 328
Concept Drift......Page 330
Evaluation Setup......Page 331
Handling Concept Drift with $NCD$......Page 333
Feature-Free Versus Feature-Based......Page 335
Conclusion......Page 337
Introduction......Page 340
How Wide Is the Vocabulary Gap in Web Search?......Page 341
Related Work......Page 342
Early Case-Based Approaches to Web Search......Page 343
A Review Collaborative Web Search......Page 344
From Selections to Snippets......Page 345
Snippet Surrogates as Cases......Page 346
Ranking and Promotion......Page 347
Experimental Data......Page 348
Systems and Setup......Page 349
A Community-Based Analysis......Page 350
Ranking Analysis......Page 351
Conclusions......Page 352
Introduction......Page 355
Model-Free Reinforcement Learning Methods......Page 356
Offline Q Learning with Value Function Approximation......Page 357
Basic Ideas of Approximate Transition Graphs......Page 358
Case-Based Transition Completion......Page 359
Learning from a Completed Case Base......Page 361
Deriving a Decision-Making Policy......Page 362
Transformational Analogy......Page 363
Empirical Evaluation......Page 365
Results......Page 366
Conclusion......Page 368
Introduction......Page 370
Anomaly Reporting......Page 371
Text Pre-processing......Page 372
Feature Selection......Page 373
Feature Extraction......Page 374
Experimental Evaluation......Page 377
Alignment Measure......Page 378
Representations......Page 380
Related Work......Page 382
Conclusions and Future Work......Page 383
Introduction and Motivation......Page 385
SPAMHUNTING System......Page 387
Previous Work on Estimating Classification Accuracy......Page 389
Defining a Relevant Information Amount Rate......Page 390
Testing Procedure......Page 391
Corpus Selection, Preprocessing Tasks and Setup Model Configuration......Page 393
Experimental Results and Evaluation......Page 394
Conclusions and Further Work......Page 397
References......Page 398
Introduction......Page 400
Related Work......Page 401
SHOMAS Architecture......Page 403
SHOMAS in Operation......Page 404
CBP-BDI Guiding Agent......Page 406
Results and Conclusions......Page 410
References......Page 413
Introduction......Page 415
Related Work......Page 416
Case-Based Reasoning in Our Approach......Page 417
Data Extraction and Coding......Page 419
Structures Extraction......Page 420
Problem Enriching Using a Set of Homogeneous Documents......Page 422
CBRDIA Cases......Page 423
Similar Case Retrieval......Page 424
KWS Solving......Page 425
The Database......Page 426
Results......Page 427
Conclusion and Future Works......Page 428
Introduction......Page 430
The Case-Based Image Segmentation Approach......Page 431
The Watershed Segmentation......Page 432
Seed Selection Based on Region Significance......Page 433
Improving Watershed Segmentation by CBR......Page 435
Case Description......Page 436
Similarity Between Cases and Retrieval......Page 437
Automatic Evaluation of the Segmentation Results......Page 438
Discussion......Page 440
Conclusion......Page 441
References......Page 442
Introduction......Page 444
Poolcasting......Page 445
The Poolcasting Web Radio Architecture......Page 447
A Case-Based Reasoning Song Scheduler......Page 448
The Participants' Case Bases......Page 449
Musical Domain Knowledge......Page 450
The Reuse Process......Page 453
Related Work......Page 456
Conclusions......Page 457
Introduction......Page 460
REBUILDER UML......Page 461
Knowledge Base......Page 463
Case Retrieval......Page 465
Example of Use......Page 466
Experiments......Page 468
Conclusions and Future Work......Page 471
Introduction......Page 474
Basic Notions, Notations and Assumptions on cbr......Page 475
Principles......Page 476
Principles of the Adaptation......Page 477
Study of an Example Through FRAKAS......Page 479
Main Algorithm of FRAKAS......Page 484
Discussion and Related Work......Page 485
Conclusion and Future Work......Page 486
Introduction......Page 489
Establishing a Person's Stress Profile......Page 490
Materials and Methods......Page 491
Classify Individual Sensitivity to Stress......Page 493
Fuzzy Classification......Page 495
Similarity Matching......Page 496
Fuzzy Matching......Page 498
Reliability of the Test......Page 499
Summary and Conclusions......Page 500
References......Page 501
Introduction......Page 503
Mémoire Project......Page 504
Cases as Contextual Knowledge......Page 505
Prototypical Case Mining......Page 506
Case Representation......Page 507
Memory Organization......Page 508
Reasoning Process......Page 509
Examples......Page 510
Example 1......Page 511
Example 2......Page 513
Discussion......Page 514
References......Page 516
Introduction......Page 518
Related Works......Page 519
Data Analysis and Categorization......Page 521
Discussion and Results......Page 524
References......Page 526
An Application of Textual Case-Based Interpretation......Page 528
Interpreting Monitoring Processes......Page 529
Knowledge Containers in TCBR......Page 531
Task Structure as Generator of Episodic Narratives......Page 533
Knowledge Extraction......Page 535
Knowledge Summarization......Page 536
Empirical Evaluation......Page 539
Discussion......Page 541
Back matter......Page 543