Matthew E. Taylor's Publications

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When Should There be a ``Me'' in ``Team''? Distributed Multi-Agent Optimization Under Uncertainty

Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yooko, and Milind Tambe. When Should There be a ``Me'' in ``Team''? Distributed Multi-Agent Optimization Under Uncertainty. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2010. 24% acceptance rate
Supplemental material is available at http://teamcore.usc.edu/dcop/.

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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.

BibTeX Entry

@inproceedings{AAMAS10-Taylor,
author    = {Matthew E. Taylor and Manish Jain and Yanquin Jin and Makoto Yooko and Milind Tambe},
title     = {When Should There be a ``Me'' in ``Team''? {D}istributed Multi-Agent Optimization Under Uncertainty},
booktitle = {Proceedings of the International Conference on Autonomous Agents and Multiagent Systems ({AAMAS})},
month="May",
year = {2010},
note = {24% acceptance rate},
wwwnote = {<a href="http://www.cse.yorku.ca/AAMAS2010/index.php>AAMAS-10</a>},
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.},
wwwnote={Supplemental material is available at <a href="http://teamcore.usc.edu/dcop/">http://teamcore.usc.edu/dcop/</a>.},
}

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