Optimal strategies for multi objective games and their search by evolutionary multi objective optimization

G. Avigad*, E. Eisenstadt, M. Weiss Cohen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

While both games and Multi-Objective Optimization (MOO) have been studied extensively in the literature, Multi-Objective Games (MOGs) have received less research attention. Existing studies deal mainly with mathematical formulations of the optimum. However, a definition and search for the representation of the optimal set, in the multi objective space, has not been attended. More specifically, a Pareto front for MOGs has not been defined or searched for in a concise way. In this paper we define such a front and propose a set-based multi-objective evolutionary algorithm to search for it. The resulting front, which is shown to be a layer rather than a clear-cut front, may support players in making strategic decisions during MOGs. Two examples are used to demonstrate the applicability of the algorithm. The results show that artificial intelligence may help solve complicated MOGs, thus highlighting a new and exciting research direction.

Original languageEnglish
Title of host publication2011 IEEE Conference on Computational Intelligence and Games, CIG 2011
Pages166-173
Number of pages8
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 7th IEEE International Conference on Computational Intelligence and Games, CIG 2011 - Seoul, Korea, Republic of
Duration: 31 Aug 20113 Sep 2011

Publication series

Name2011 IEEE Conference on Computational Intelligence and Games, CIG 2011

Conference

Conference2011 7th IEEE International Conference on Computational Intelligence and Games, CIG 2011
Country/TerritoryKorea, Republic of
CitySeoul
Period31/08/113/09/11

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