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dc.contributor.authorAfsar, Bekir
dc.contributor.authorPodkopaev, Dmitry
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorCristani, Matteo
dc.contributor.editorToro, Carlos
dc.contributor.editorZanni-Merk, Cecilia
dc.contributor.editorHowlett, Robert J.
dc.contributor.editorJain, Robert J.
dc.date.accessioned2020-10-09T07:34:37Z
dc.date.available2020-10-09T07:34:37Z
dc.date.issued2020
dc.identifier.citationAfsar, B., Podkopaev, D., & Miettinen, K. (2020). Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture. In M. Cristani, C. Toro, C. Zanni-Merk, R. J. Howlett, & R. J. Jain (Eds.), <i>KES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems</i> (pp. 281-290). Elsevier BV. Procedia Computer Science, 176. <a href="https://doi.org/10.1016/j.procs.2020.08.030" target="_blank">https://doi.org/10.1016/j.procs.2020.08.030</a>
dc.identifier.otherCONVID_42394854
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72082
dc.description.abstractIn many decision making problems, a decision maker needs computer support in finding a good compromise between multiple conflicting objectives that need to be optimized simultaneously. Interactive multiobjective optimization methods have a lot of potential for solving such problems. However, the growth of complexity in problem formulations and the abundance of data bring new challenges to be addressed by decision makers and method developers. On the other hand, advances in the field of artificial intelligence provide opportunities in this respect. We identify challenges and propose directions of addressing them in interactive multiobjective optimization methods with the help of multiple intelligent agents. We describe a generic architecture of enhancing interactive methods with specialized agents to enable more efficient and reliable solution processes and better support for decision makers.en
dc.format.extent3880
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofKES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
dc.relation.ispartofseriesProcedia Computer Science
dc.rightsCC BY-NC-ND 4.0
dc.subject.othermultiple criteria optimization
dc.subject.otherinteractive methods
dc.subject.otherdecision support
dc.subject.otherdata-driven decision making
dc.subject.othercomputational intelligence
dc.subject.otheragents
dc.subject.othermulti-agent systems
dc.titleData-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202010096137
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange281-290
dc.relation.issn1877-0509
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 The Author(s). Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Knowledge-Based and Intelligent Information & Engineering Systems
dc.relation.grantnumber322221
dc.relation.grantnumber311877
dc.subject.ysointeraktiivisuus
dc.subject.ysopäätöksenteko
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoälykkäät agentit
dc.subject.ysopäätöksentukijärjestelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p10823
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p24489
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.procs.2020.08.030
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis research was partly funded by the Academy of Finland (grants 311877 and 322221). The research is relatedto the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.


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CC BY-NC-ND 4.0
Except where otherwise noted, this item's license is described as CC BY-NC-ND 4.0