Using geometric diffusions for recognition-primed multi-agent decision making

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.

[1]  Ann B. Lee,et al.  Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Ronald R. Coifman,et al.  Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators , 2005, NIPS.

[3]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[4]  John Yen,et al.  R-CAST: Integrating Team Intelligence for Human-Centered Teamwork , 2007, AAAI.

[5]  Sridhar Mahadevan,et al.  Multiscale analysis of document corpora based on diffusion models , 2009, IJCAI 2009.

[6]  Marie desJardins,et al.  Agent-organized networks for dynamic team formation , 2005, AAMAS '05.

[7]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[8]  Javier Vázquez-Salceda,et al.  Organizing Multiagent Systems , 2005, Autonomous Agents and Multi-Agent Systems.

[9]  Gary Klein,et al.  Sources of Power: How People Make Decisions , 2017 .

[10]  R. Coifman,et al.  Diffusion Wavelets , 2004 .

[11]  Emma Norling,et al.  Folk psychology for human modelling: extending the BDI paradigm , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[12]  Jordi Sabater-Mir,et al.  Reputation and social network analysis in multi-agent systems , 2002, AAMAS '02.

[13]  B. Nadler,et al.  Diffusion maps, spectral clustering and reaction coordinates of dynamical systems , 2005, math/0503445.

[14]  Paul Scerri,et al.  Towards Flexible Coordination of Large Scale Multi-Agent Teams , 2006 .

[15]  Arthur D. Szlam,et al.  Diffusion wavelet packets , 2006 .

[16]  Gary Klein,et al.  Naturalistic Decision Making , 2008, Hum. Factors.

[17]  Paul Scerri,et al.  Agent Organized Networks Redux , 2008, AAAI.