Kagan Tumer's Publications

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The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination. J. J. Chung, D. Miklic, L. Sabattini, K. Tumer, and R. Siegwart. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, pp. , Montreal, Canada, May 2019.

Abstract

The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions.

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BibTeX Entry

@InProceedings{tumer-chung_aamas19,
author = {J. J. Chung and D. Miklic and L. Sabattini and K. Tumer and R. Siegwart},
title = {The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination},
booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems},
address = {Montreal, Canada},
month = {May},
 pages={},
 abstract={The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions.},
	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Multiagent Systems},
year = {2019}
}

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