| CONTENTS | 10 |
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| TECHNICAL KEYNOTE ADDRESS | 13 |
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| BEST TECHNICAL PAPER | 15 |
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| An Evolutionary Algorithm-Based Approach to Robust Analog Circuit Design using Constrained Multi- Objective Optimization | 16 |
| 1 Introduction | 16 |
| 2 Nominal Design versus Design for Yield | 18 |
| 3 IC Design as a Constrained MOP | 19 |
| 4 The Algorithm | 21 |
| 5 Radio Frequency Low Noise Amplifier | 22 |
| 6 LeapFrog Filter | 24 |
| 7 Ultra WideBand Low Noise Amplifier | 26 |
| 8 Conclusions | 28 |
| Acknowledgment | 28 |
| References | 28 |
| CONSTRAINT SATISFACTION | 30 |
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| Dynamic Rule Mining for Argumentation Based Systems | 72 |
| 1 Introduction | 72 |
| 2 Previous Work | 73 |
| 3 PADUA Protocol | 77 |
| 4 Dynamic Association Rules Generation | 79 |
| 5 Experimentation and Analysis | 82 |
| 6 Conclusions | 83 |
| References | 84 |
| AI TECHNIQUES | 86 |
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| Learning Sets of Sub-Models for Spatio- Temporal Prediction | 127 |
| 1 Introduction | 127 |
| 2 Architecture for Models of Spatio-Temporal Data | 130 |
| 3 Learning the Models from Data | 132 |
| 4 Evaluation | 134 |
| 5 Results | 136 |
| 6 Conclusions | 138 |
| References | 139 |
| DATA MINING AND MACHINE LEARNING | 141 |
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| Frequent Set Meta Mining: Towards Multi-Agent Data Mining | 142 |
| 1 Introduction | 142 |
| 2 Previous Work | 143 |
| 3 Note on P and T Trees | 145 |
| 4 Proposed Meta ARM Algorithms | 146 |
| 5 Experimentation and Analysis | 149 |
| 6 Conclusions | 153 |
| References | 153 |
| A Flexible Framework To Experiment WithOntology Learning Techniques | 155 |
| Evolving a Dynamic Predictive Coding Mechanism for Novelty Detection | 169 |
| MULTI-AGENT SYSTEMS | 197 |
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| Merging Intelligent Agency and the Semantic Web | 198 |
| Expressive security policy rules usingLayered Conceptual Graphs | 238 |
| 1 Introduction | 238 |
| 2 Motivation and background | 239 |
| 3 Security mechanisms for HealthAgents | 241 |
| 4 Policy rules with Conceptual Graphs | 245 |
| 5 Conclusions and future work | 250 |
| 6 Acknowledgements | 250 |
| References | 250 |
| DATA MINING | 252 |
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| Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval | 253 |
| 1 Introduction | 253 |
| 2 Association Rules | 255 |
| 3 Related Work | 255 |
| 4 Using Concepts from Information Retrieval | 257 |
| 5 Rule Representation | 258 |
| 6 Pairwise Similarity | 259 |
| 7 Similarity Aggregation | 260 |
| 8 Relevance Scoring | 262 |
| 9 Conclusion | 264 |
| References | 264 |
| Visualization and Grouping of Graph Patterns in Molecular Databases | 267 |
| 1 Introduction | 267 |
| 2 Distance Measure | 269 |
| 3 Optimization: Restriction to Frequent Subgraphs and Grouping | 270 |
| 4 Visualization | 272 |
| 5 Performance | 273 |
| 6 Conclusions and Future Work | 277 |
| References | 278 |
| A Classification Algorithm based on Concept Similarity | 281 |
| KNOWLEDGE ACQUISITION AND MANAGEMENT | 303 |
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| Knowledge Management for Evolving Products | 304 |
| Recovery from Plan Failures in Partially Observable Environments | 318 |
| Automatic Character Assignation | 332 |
| SHORT PAPERS | 346 |
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| An Agent-Based Algorithm for Data Reduction | 347 |
| Towards a Computationally Efficient Approach to Modular Classification Rule Induction | 353 |
| Spatial N-player Dilemmas in Changing Environments | 377 |
| 1 Introduction | 377 |
| 2 Related Work | 378 |
| 3 Model and Experimental Set up | 379 |
| 4 Results | 380 |
| 5 Conclusion | 381 |
| References | 381 |
