Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture
| dc.contributor.author | Afsar, Bekir | |
| dc.contributor.author | Podkopaev, Dmitry | |
| dc.contributor.author | Miettinen, Kaisa | |
| dc.contributor.editor | Cristani, Matteo | |
| dc.contributor.editor | Toro, Carlos | |
| dc.contributor.editor | Zanni-Merk, Cecilia | |
| dc.contributor.editor | Howlett, Robert J. | |
| dc.contributor.editor | Jain, Robert J. | |
| dc.date.accessioned | 2020-10-09T07:34:37Z | |
| dc.date.available | 2020-10-09T07:34:37Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Afsar, 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.other | CONVID_42394854 | |
| dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/72082 | |
| dc.description.abstract | In 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.extent | 3880 | |
| dc.format.mimetype | application/pdf | |
| dc.language | eng | |
| dc.language.iso | eng | |
| dc.publisher | Elsevier BV | |
| dc.relation.ispartof | KES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems | |
| dc.relation.ispartofseries | Procedia Computer Science | |
| dc.rights | CC BY-NC-ND 4.0 | |
| dc.subject.other | multiple criteria optimization | |
| dc.subject.other | interactive methods | |
| dc.subject.other | decision support | |
| dc.subject.other | data-driven decision making | |
| dc.subject.other | computational intelligence | |
| dc.subject.other | agents | |
| dc.subject.other | multi-agent systems | |
| dc.title | Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture | |
| dc.type | conference paper | |
| dc.identifier.urn | URN:NBN:fi:jyu-202010096137 | |
| dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
| dc.contributor.laitos | Faculty of Information Technology | en |
| dc.contributor.oppiaine | Multiobjective Optimization Group | fi |
| dc.contributor.oppiaine | Laskennallinen tiede | fi |
| dc.contributor.oppiaine | Päätöksen teko monitavoitteisesti | fi |
| dc.contributor.oppiaine | Multiobjective Optimization Group | en |
| dc.contributor.oppiaine | Computational Science | en |
| dc.contributor.oppiaine | Decision analytics utilizing causal models and multiobjective optimization | en |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
| dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
| dc.description.reviewstatus | peerReviewed | |
| dc.format.pagerange | 281-290 | |
| dc.relation.issn | 1877-0509 | |
| dc.type.version | publishedVersion | |
| dc.rights.copyright | © 2020 The Author(s). Published by Elsevier B.V. | |
| dc.rights.accesslevel | openAccess | fi |
| dc.type.publication | conferenceObject | |
| dc.relation.conference | International Conference on Knowledge-Based and Intelligent Information & Engineering Systems | |
| dc.relation.grantnumber | 322221 | |
| dc.relation.grantnumber | 311877 | |
| dc.subject.yso | interaktiivisuus | |
| dc.subject.yso | päätöksenteko | |
| dc.subject.yso | monitavoiteoptimointi | |
| dc.subject.yso | älykkäät agentit | |
| dc.subject.yso | päätöksentukijärjestelmät | |
| dc.format.content | fulltext | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p10823 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p8743 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p32016 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p24489 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p27803 | |
| dc.rights.url | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.relation.doi | 10.1016/j.procs.2020.08.030 | |
| dc.relation.funder | Research Council of Finland | en |
| dc.relation.funder | Research Council of Finland | en |
| dc.relation.funder | Suomen Akatemia | fi |
| dc.relation.funder | Suomen Akatemia | fi |
| jyx.fundingprogram | Academy Project, AoF | en |
| jyx.fundingprogram | Research profiles, AoF | en |
| jyx.fundingprogram | Akatemiahanke, SA | fi |
| jyx.fundingprogram | Profilointi, SA | fi |
| jyx.fundinginformation | This 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. | |
| dc.type.okm | A4 |
Aineistoon kuuluvat tiedostot
Aineisto kuuluu seuraaviin kokoelmiin
Ellei muuten mainita, aineiston lisenssi on CC BY-NC-ND 4.0