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Solving decentralized multi-agent control problems with genetic algorithms

Publication ,  Journal Article
Mazurowski, MA; Zurada, JM
Published in: 2007 IEEE Congress on Evolutionary Computation, CEC 2007
December 1, 2007

In decentralized control of multi-agent systems each agent is making a decision regarding its action autonomously, based on its own observations. In the light of the formal models of decentralized environments presented in the last decade, finding an optimal solution to a decentralized control problem is computationally prohibitive, even for moderately complicated environments. The problem, however, is of great significance since many of the real world systems can be treated as multi-agent systems with decentralized control. In this article, the authors propose an approximate algorithm for the problem based on a genetic algorithm. First, the problem is formalized using Decentralized Partially Observable Markov Decision Processes. Then a way of representing a solution (joint policy) in a chromosome is introduced and a genetic algorithm is proposed as a search mechanism. Finally, a multi-agent tiger problem is used as an experimental framework to show the effectiveness of the algorithm. © 2007 IEEE.

Duke Scholars

Published In

2007 IEEE Congress on Evolutionary Computation, CEC 2007

DOI

Publication Date

December 1, 2007

Start / End Page

1029 / 1034
 

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Mazurowski, M. A., & Zurada, J. M. (2007). Solving decentralized multi-agent control problems with genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 1029–1034. https://doi.org/10.1109/CEC.2007.4424583
Mazurowski, M. A., and J. M. Zurada. “Solving decentralized multi-agent control problems with genetic algorithms.” 2007 IEEE Congress on Evolutionary Computation, CEC 2007, December 1, 2007, 1029–34. https://doi.org/10.1109/CEC.2007.4424583.
Mazurowski MA, Zurada JM. Solving decentralized multi-agent control problems with genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007 Dec 1;1029–34.
Mazurowski, M. A., and J. M. Zurada. “Solving decentralized multi-agent control problems with genetic algorithms.” 2007 IEEE Congress on Evolutionary Computation, CEC 2007, Dec. 2007, pp. 1029–34. Scopus, doi:10.1109/CEC.2007.4424583.
Mazurowski MA, Zurada JM. Solving decentralized multi-agent control problems with genetic algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007 Dec 1;1029–1034.

Published In

2007 IEEE Congress on Evolutionary Computation, CEC 2007

DOI

Publication Date

December 1, 2007

Start / End Page

1029 / 1034