A4 Artikkeli konferenssijulkaisussa
Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture (2020)

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.), KES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (pp. 281-290). Elsevier BV. Procedia Computer Science, 176. https://doi.org/10.1016/j.procs.2020.08.030

JYU-tekijät tai -toimittajat

Afsar, Bekir
Miettinen, Kaisa

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Afsar, Bekir; Podkopaev, Dmitry; Miettinen, Kaisa

Emojulkaisu: KES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems

Emojulkaisun toimittajat: Cristani, Matteo; Toro, Carlos; Zanni-Merk, Cecilia; Howlett, Robert J.; Jain, Robert J.

Konferenssin paikka ja aika: Virtual conference, 16.-18.9.2020

Lehti tai sarja: Procedia Computer Science

ISSN: 1877-0509

eISSN: 1877-0509

Julkaisuvuosi: 2020

Sarjan numero: 176

Artikkelin sivunumerot: 281-290

Kirjan kokonaissivumäärä: 3880

Kustantaja: Elsevier BV

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

DOI: https://doi.org/10.1016/j.procs.2020.08.030

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72082

Tiivistelmä

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.

YSO-asiasanat: monitavoiteoptimointi; päätöksenteko; päätöksentukijärjestelmät; interaktiivisuus; älykkäät agentit

Vapaat asiasanat: multiple criteria optimization; interactive methods; decision support; data-driven decision making; computational intelligence; agents; multi-agent systems

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