Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Folk Psychology for Human Modelling: Extending the BDI Paradigm
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Autonomous Agents and Multi-Agent Systems
R-CAST: integrating team intelligence for human-centered teamwork
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Agent organized networks redux
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Multiscale analysis of document corpora based on diffusion models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The Impact of Splitting Granularity on Multi-agent Diffusion Computing
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Decision as choice of potential intentions
Web Intelligence and Agent Systems
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Several areas of multi-agent research, such as large-scale agent organization and experience-based decision making, demand novel perspectives and efficient approaches for multiscale information analysis. A recent breakthrough in harmonic analysis is diffusion geometry and diffusion wavelets, which offers a general framework for multiscale analysis of massive data sets. In this paper, we introduce the "diffusion" concept into the MAS field, and investigate the impacts of using diffusion distance on the performance of solution synthesis in experience-based multi-agent decision making. In particular, we take a two-dimensional perspective to explore the use of diffusion distance and Euclidean distance in identifying 'similar' experiences--a key activity in the process of recognition-primed decision making. An experiment has been conducted on a data set including a large collection of battlefield decision experiences. It is shown that the performance of using diffusion distance can be significantly better than using Euclidean distance in the original experience space. This study allows us to generalize an anytime algorithm for multi-agent decision making, and it also opens the door to the application of diffusion geometry to multiagent research involving massive data analysis.