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Designing Games for Distributed Optimization

Li, Na and Marden, Jason R. (2013) Designing Games for Distributed Optimization. IEEE Journal of Selected Topics in Signal Processing, 7 (2). pp. 230-242. ISSN 1932-4553. doi:10.1109/JSTSP.2013.2246511.

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The central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to a given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent's control law on the least amount of information possible. This paper focuses on achieving this goal using the field of game theory. In particular, we derive a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting Nash equilibria and the optimizers of the system level objective and (ii) that the resulting game possesses an inherent structure that can be exploited in distributed learning, e.g., potential games. The control design can then be completed utilizing any distributed learning algorithm which guarantees convergence to a Nash equilibrium for the attained game structure. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

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Additional Information:© 2013 IEEE. Manuscript received August 16, 2012; revised November 30, 2012; accepted January 26, 2013. Date of publication February 11, 2013; date of current version March 09, 2013. This work was supported in part by the Air Force Office of Scientific Research (AFOSR) under Grants FA9550-09-1-0538 and FA9550-12-1-0359 and in part by the Office of Naval Research (ONR) under Grant N00014-12-1-0643. The conference version of this work appeared in [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Isao Yamada.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA9550-09-1-0538
Air Force Office of Scientific Research (AFOSR)FA9550-12-1-0359
Office of Naval Research (ONR)N00014-12-1-0643
Subject Keywords:Distributed optimization; multi-agent system; game theory
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INSPEC Accession Number13382432
Issue or Number:2
Record Number:CaltechAUTHORS:20130610-090502368
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Official Citation:Na Li; Marden, J.R., "Designing Games for Distributed Optimization," Selected Topics in Signal Processing, IEEE Journal of , vol.7, no.2, pp.230,242, April 2013 doi: 10.1109/JSTSP.2013.2246511
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:38870
Deposited By: Tony Diaz
Deposited On:24 Jun 2013 23:03
Last Modified:09 Nov 2021 23:40

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