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The performance of distributed systems generally depend on the actions and interactions of a large number of independent components (e.g., agents, neurons). Such "collectives" are often subject to communication restrictions, making it difficult for the components to coordinate their actions to provide good system level performance. In this article we address that coordination problem and derive four agent utility functions that make different tradeoffs between alignedness between agent and system utilities and the signal-to-noise each agent encounters. The results show that these utility functions outperform both traditional methods and previous collective-based methods by up to 75\% in systems with communication restrictions.
@inproceedings{tumer-agogino_ijcnn04,
author = {K. Tumer and A. Agogino},
title = {Overcoming Communication Restrictions in Collectives},
booktitle = {Proceedings of the International Joint Conference on
Neural Networks},
month = {July},
address = {Budapest, Hungary},
abstract = {The performance of distributed systems generally depend on the actions and interactions of a large number of independent components (e.g., agents, neurons). Such "collectives" are often subject to communication restrictions, making it difficult for the components to coordinate their actions to provide good system level performance. In this article we address that coordination problem and derive four agent utility functions that make different tradeoffs between alignedness between agent and system utilities and the signal-to-noise each agent encounters. The results show that these utility functions outperform both traditional methods and previous collective-based methods by up to 75\% in systems with communication restrictions.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Collectives},
year = {2004}
}
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