When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty

  • Authors:

  • Matthew E. Taylor;Manish Jain;Yanquin Jin;Makoto Yokoo;Milind Tambe

  • Affiliations:

  • University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Tsinghua University, Beijing, China;Kyushu University, Fukuoka, Japan;University of Southern California, Los Angeles, CA

  • Venue:
  • Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Increasing teamwork between agents typically increases the performance of a multi-agent system, at the cost of increased communication and higher computational complexity. This work examines joint actions in the context of a multi-agent optimization problem where agents must cooperate to balance exploration and exploitation. Surprisingly, results show that increased teamwork can hurt agent performance, even when communication and computation costs are ignored, which we term the team uncertainty penalty. This paper introduces the above phenomena, analyzes it, and presents algorithms to reduce the effect of the penalty in our problem setting.