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Decentralized computation of pareto optimal pure nash equilibria of boolean games with privacy concerns

De Clercq, Sofie, Bauters, Kim, Schockaert, Steven, Mihaylov, Mihail, De Cock, Martine and Nowe, Ann 2014. Decentralized computation of pareto optimal pure nash equilibria of boolean games with privacy concerns. Presented at: International Conference on Agents and Artificial Intelligence (ICAART 2014), Angers, France, 6-8 March 2014. Published in: Duval, Beatrice, van den Herik, Jaap, Loiseau, Stephane and Filipe, Joaquim eds. Issue Proceedings ICAART 2014. pp. 50-59. 10.5220/0004811700500059

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Abstract

In Boolean games, agents try to reach a goal formulated as a Boolean formula. These games are attractive because of their compact representations. However, few methods are available to compute the solutions and they are either limited or do not take privacy or communication concerns into account. In this paper we propose the use of an algorithm related to reinforcement learning to address this problem. Our method is decentralized in the sense that agents try to achieve their goals without knowledge of the other agents’ goals. We prove that this is a sound method to compute a Pareto optimal pure Nash equilibrium for an interesting class of Boolean games. Experimental results are used to investigate the performance of the algorithm.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Boolean Games, Pure Nash Equilibria, Decentralized Learning.
Last Modified: 04 Jun 2017 07:50
URI: http://orca.cf.ac.uk/id/eprint/68589

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