Decentralized Computation of Pareto Optimal Pure Nash Equilibria of Boolean Games with Privacy Concerns

Sofie De Clercq, Kim Bauters, Steven Schockaert, Mihail Mihaylov, Martine De Cock, Ann Nowé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART)
EditorsBeatrice Duval, Jaap van der Herik, Stephane Loiseau, Joaquim Filipe
PublisherSciTePress
Pages50-59
Number of pages10
ISBN (Print)9789897580161
DOIs
Publication statusPublished - 2014
Event6th International Conference on Agents and Artifcial Intelligence - Angers, France
Duration: 06 Mar 201408 Mar 2014

Conference

Conference6th International Conference on Agents and Artifcial Intelligence
CountryFrance
CityAngers
Period06/03/201408/03/2014

Bibliographical note

short-listed for best student paper at ICAART'14

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