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

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. Categories and Subject Descriptors I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence-Intelligent agents General Terms Algorithms, Experimentation Keywords Coordination, DCEE, DCOP, Multi-agent Exploration and Exploitation, Multi-agent Optimization

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yokoo, Milind Tambe
• Keywords: ATAL 2010, Intelligent Agents, Multi-agent, Multi-agent Optimization, Multi-agent Optimization Problem