Bramer / Coenen / Petridis Research and Development in Intelligent Systems XXIV

Proceedings of AI-2007, The Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
1. Auflage 2007
ISBN: 978-1-84800-094-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 398 Seiten

An agent in a multi-agent system (MAS) has to generate plans for its individual goal, but these plans may con?ict with those that are already being scheduled or executed by other agents. It must also be able to complete its planning and resolution of these con?icts within a reasonable time to have an acceptable quality plan. Although we adopt hierarchical planning (HP, for example, see [7, 12]) using the decision-theoretic planning (DTP) approach [6] for ef?cient planning, it is not trivial to apply HPO to MAS. In HP, appropriate (abstract) plans are selected level by level to maximize the utility U (p), where where p is the expected ?nal plan comprising a sequence of primitive actions. However, in the MAS context, con?icts between agents affect the ef?ciency and quality of resulting plans. When a con?ict is found at lower levels, an additional sophisticated process for avoiding it (con?ict resolution) must be invoked and some extra actions (such as waiting for synchronization and detouring) may have to be added to the plan. The con?ict resolution process may become costly or fail. Even a single con?ict, if it is dif?cult to resolve, will result in a plan with considerably lower quality than it otherwise would have. As a result, in multi-agent systems, the second- or third-best plans may result in better overall performance.

Autoren/Hrsg.

Bramer, Max

Coenen, Frans

Petridis, Miltos

Inhaltsverzeichnis

1;CONTENTS;10
2;TECHNICAL KEYNOTE ADDRESS;13
3;BEST TECHNICAL PAPER;15
3.1;An Evolutionary Algorithm-Based Approach to Robust Analog Circuit Design using Constrained Multi- Objective Optimization;16
3.1.1;1 Introduction;16
3.1.2;2 Nominal Design versus Design for Yield;18
3.1.3;3 IC Design as a Constrained MOP;19
3.1.4;4 The Algorithm;21
3.1.5;5 Radio Frequency Low Noise Amplifier;22
3.1.6;6 LeapFrog Filter;24
3.1.7;7 Ultra WideBand Low Noise Amplifier;26
3.1.8;8 Conclusions;28
3.1.9;Acknowledgment;28
3.1.10;References;28
4;CONSTRAINT SATISFACTION;30
4.1;Dynamic Rule Mining for Argumentation Based Systems;72
4.1.1;1 Introduction;72
4.1.2;2 Previous Work;73
4.1.3;3 PADUA Protocol;77
4.1.4;4 Dynamic Association Rules Generation;79
4.1.5;5 Experimentation and Analysis;82
4.1.6;6 Conclusions;83
4.1.7;References;84
5;AI TECHNIQUES;86
5.1;Learning Sets of Sub-Models for Spatio- Temporal Prediction;127
5.1.1;1 Introduction;127
5.1.2;2 Architecture for Models of Spatio-Temporal Data;130
5.1.3;3 Learning the Models from Data;132
5.1.4;4 Evaluation;134
5.1.5;5 Results;136
5.1.6;6 Conclusions;138
5.1.7;References;139
6;DATA MINING AND MACHINE LEARNING;141
6.1;Frequent Set Meta Mining: Towards Multi-Agent Data Mining;142
6.1.1;1 Introduction;142
6.1.2;2 Previous Work;143
6.1.3;3 Note on P and T Trees;145
6.1.4;4 Proposed Meta ARM Algorithms;146
6.1.5;5 Experimentation and Analysis;149
6.1.6;6 Conclusions;153
6.1.7;References;153
6.2;A Flexible Framework To Experiment WithOntology Learning Techniques;155
6.3;Evolving a Dynamic Predictive Coding Mechanism for Novelty Detection;169
7;MULTI-AGENT SYSTEMS;197
7.1;Merging Intelligent Agency and the Semantic Web;198
7.2;Expressive security policy rules usingLayered Conceptual Graphs;238
7.2.1;1 Introduction;238
7.2.2;2 Motivation and background;239
7.2.3;3 Security mechanisms for HealthAgents;241
7.2.4;4 Policy rules with Conceptual Graphs;245
7.2.5;5 Conclusions and future work;250
7.2.6;6 Acknowledgements;250
7.2.7;References;250
8;DATA MINING;252
8.1;Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval;253
8.1.1;1 Introduction;253
8.1.2;2 Association Rules;255
8.1.3;3 Related Work;255
8.1.4;4 Using Concepts from Information Retrieval;257
8.1.5;5 Rule Representation;258
8.1.6;6 Pairwise Similarity;259
8.1.7;7 Similarity Aggregation;260
8.1.8;8 Relevance Scoring;262
8.1.9;9 Conclusion;264
8.1.10;References;264
8.2;Visualization and Grouping of Graph Patterns in Molecular Databases;267
8.2.1;1 Introduction;267
8.2.2;2 Distance Measure;269
8.2.3;3 Optimization: Restriction to Frequent Subgraphs and Grouping;270
8.2.4;4 Visualization;272
8.2.5;5 Performance;273
8.2.6;6 Conclusions and Future Work;277
8.2.7;References;278
8.3;A Classification Algorithm based on Concept Similarity;281
9;KNOWLEDGE ACQUISITION AND MANAGEMENT;303
9.1;Knowledge Management for Evolving Products;304
9.2;Recovery from Plan Failures in Partially Observable Environments;318
9.3;Automatic Character Assignation;332
10;SHORT PAPERS;346
10.1;An Agent-Based Algorithm for Data Reduction;347
10.2;Towards a Computationally Efficient Approach to Modular Classification Rule Induction;353
10.3;Spatial N-player Dilemmas in Changing Environments;377
10.3.1;1 Introduction;377
10.3.2;2 Related Work;378
10.3.3;3 Model and Experimental Set up;379
10.3.4;4 Results;380
10.3.5;5 Conclusion;381
10.3.6;References;381