Kagan Tumer's Publications

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Coordination and Control for Large Distributed Sensor Networks. M. Colby, C. Holmes Parker, and K. Tumer. In Future of Instrumentation International Workshop (FIIW-2012), Gatlinburg, TN, October 2012.

Abstract

As the complexity of power plants increase, so does the difficulty in accurately modeling the interactions among the subsystems. Distributed sensing and control offers a possible solution to this problem, but introduces a new one: how to ensure that each subsystem satisfying its control objective leads to the safe and reliable operation of the entire power plant.In this work we present a distributed coordination algorithm that offers safe, reliable, and scalable control of a distributed system. In this approach, each system component uses a reinforcement learning algorithms to achieve its own objectives, but those objectives are derived to coordinate implicitly and achieve the system level objective. We show that in a Time-Extended Defect Combination Problem where the agents need to determine when and whether or not they should be sensing in order to maintain QoS in a system, the proposed method outperforms traditional methods by up to two orders of magnitude.

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BibTeX Entry

@incollection{tumer-colby_fiiw12,
        author = {M. Colby and C. Holmes Parker   and K. Tumer},
        title = {Coordination and Control for Large Distributed Sensor Networks},
        booktitle = {Future of Instrumentation International Workshop (FIIW-2012)},
	month = {October},
	address = {Gatlinburg, TN},
	editors = {K. Tobin Jr.},
	abstract={As the complexity of power plants increase, so does the  difficulty in accurately modeling the interactions among the subsystems.  Distributed sensing and control  offers a possible solution to this problem, but introduces a new one: how to ensure that each subsystem satisfying its control objective leads to the safe and reliable operation of the entire power plant.
In this work we present a distributed  coordination algorithm that offers safe, reliable,  and scalable control of a distributed system. In this approach, each system component uses a reinforcement learning algorithms to achieve its own objectives, but those objectives are derived to coordinate implicitly and achieve the system level objective. We show that in a Time-Extended Defect Combination Problem where the agents need to determine when and whether or not they should be sensing in order to maintain QoS in a system, the proposed method outperforms traditional methods by up to two orders of magnitude.},
	bib2html_pubtype = {Workshop/Symposium Papers},
	bib2html_rescat = {Multiagent Systems, Complex Systems},
        year = {2012}
}

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