Solving Multi-Objective Games using a-priori auxiliary criteria

Meir Harel, Erella Matalon-Eisenstadt, Amiram Moshaiov

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

5 Scopus citations

Abstract

This paper describes a method to support strategy selection in zero-sum Multi-Objective Games (MOGs). It follows a recent development concerning the solution of MOGs based on a novel non-utility approach. Such an approach commonly results with a large set of rationalizable strategies to choose from. Here, this approach is further developed to narrow down the set of rationalizable strategies into a set of preferable strategies using a-priori incorporation of decision-makers' preferences (auxiliary criteria). To search for the latter set a co-evolutionary algorithm is devised. The effectiveness of the algorithm is studied using an academic example of a zero-sum MOG involving two manipulators. To test the algorithm, a validation method is suggested using a discrete version of the example. The results substantiate the claim that the proposed algorithm finds a good approximation of the set of preferable strategies.

Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1428-1435
Number of pages8
ISBN (Electronic)9781509046010
DOIs
StatePublished - 5 Jul 2017
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Publication series

Name2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings

Conference

Conference2017 IEEE Congress on Evolutionary Computation, CEC 2017
Country/TerritorySpain
CityDonostia-San Sebastian
Period5/06/178/06/17

Keywords

  • Multi-crieteria decision-making
  • Multi-objective game
  • Multi-payoff game
  • Non-cooperative game
  • Pareto optimization
  • Rationalizable strategies

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