A batch Bayesian approach for bilevel multi-objective decision making under uncertainty

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dc.contributor.authorDogan, Vedaten
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-07-11T14:24:46Z
dc.date.available2023-07-11T14:24:46Z
dc.date.issued2023-02-13en
dc.description.abstractBilevel multiobjective optimization is a field of mathematical programming representing a nested hierarchical decision making process, with one or more decision makers at each level. These problems appear in many practical applications, solving tasks such as optimal control, process optimization, governmental and game playing strategy development, and transportation. Uncertainty cannot be ignored in these practical problems. We present a hybrid algorithm called BAM- BINO, based on a batch Bayesian approach via expected hyper-volume improvement, that can handle uncertainty at the upper level. Three popular modified benchmark problems with multiple dimensions are used to evaluate its performance under objective noise compared to two popular algorithms in the literature. The results show that BAMBINO is computationally efficient and able to handle upper level uncertainty. We also evaluate the effect of batch size on performance.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDogan, V. and Prestwich, S. D. (2023) 'A batch Bayesian approach for bilevel multi-objective decision making under uncertainty', AAAI 23: Thirty-Seventh AAAI Conference on Artificial Intelligence, 1st AAAI Workshop on Uncertainty Reasoning and Quantification in Decision Making, 7-14 February, Washington DC, USA.en
dc.identifier.endpage5en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14711
dc.language.isoenen
dc.publisherAssociation for the Advancement of Artificial Intelligence; AAAIen
dc.relation.ispartofAAAI 23: Thirty-Seventh AAAI Conference on Artificial Intelligenceen
dc.relation.ispartof1st AAAI Workshop on Uncertainty Reasoning and Quantification in Decision Makingen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2023 the authors. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission; Published version: © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBayesian optimizationen
dc.subjectBilevel optimization problemsen
dc.subjectMulti-objective acquisitionen
dc.subjectMulti-objective optimizationen
dc.subjectBAMBINOen
dc.subjectUncertaintyen
dc.subjectBatch Bayesian approachen
dc.titleA batch Bayesian approach for bilevel multi-objective decision making under uncertaintyen
dc.typeConference itemen
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