On the variation of max regret with respect to the scaling of the objectives

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dc.contributor.authorWilson, Nicen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderHorizon 2020 Framework Programmeen
dc.date.accessioned2023-11-03T12:42:50Z
dc.date.available2023-11-03T12:42:50Z
dc.date.issued2023-10en
dc.description.abstractIn a multi-objective optimisation problem, when there is uncertainty regarding the correct user preference model, max regret is a natural measure for how far an alternative is from being necessarily optimal (i.e., optimal with respect to every candidate preference model). It can be used for recommending a relatively safe choice to the user, or used in the generation of an informative query, and in the decision to terminate the user interaction, because an alternative is sufficiently close to being necessarily optimal. We consider a common and simple form of user preference model: a weighted average over the objectives (with unknown weights). However, changing the scale of an objective by a linear factor leads to an essentially different set of preference models, and this changes the max regret values (and potentially their relative ordering), sometimes very considerably. Since the scaling of the objectives is often partly subjective and somewhat arbitrary, it is important to be aware of how sensitive the max regret values are to the choices of scaling of the objectives. We give mathematical results that characterise and enable computation of this variability, along with an asymptotic analysis.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWilson, N. (2023) ‘On the variation of max regret with respect to the scaling of the objectives’, in K. Gal, A. Nowé, G.J. Nalepa, R. Fairstein, and R. Rădulescu (eds.), Frontiers in Artificial Intelligence and Applications, Volume 372: ECAI 2023, IOS Press, pp. 2639 - 2646. https://doi.org/10.3233/FAIA230571en
dc.identifier.doi10.3233/faia230571en
dc.identifier.endpage2646en
dc.identifier.isbn9781643684369en
dc.identifier.isbn9781643684376en
dc.identifier.issn0922-6389en
dc.identifier.issn1879-8314en
dc.identifier.startpage2639en
dc.identifier.urihttps://hdl.handle.net/10468/15187
dc.language.isoenen
dc.publisherIOS Pressen
dc.relation.ispartofFrontiers in Artificial Intelligence and Applicationsen
dc.relation.ispartofECAI 2023, the 26th European Conference on Artificial Intelligenceen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/952215/EU/Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization/TAILORen
dc.relation.urihttps://doi.org/10.3233/FAIA230571en
dc.rights© 2023 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.enen
dc.subjectMulti-objective optimisation problemen
dc.subjectArtificial intelligenceen
dc.subjectAIen
dc.subjectMax regreten
dc.subjectUser preference modelen
dc.titleOn the variation of max regret with respect to the scaling of the objectivesen
dc.typeConference itemen
dc.typebook-chapteren
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