A4 Article in conference proceedings
Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture (2020)


Afsar, Bekir; Podkopaev, Dmitry; Miettinen, Kaisa (2020). Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture. In Cristani, Matteo; Toro, Carlos; Zanni-Merk, Cecilia; Howlett, Robert J.; Jain, Robert J. (Eds.) KES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Procedia Computer Science, 176. Elsevier BV, 281-290. DOI: 10.1016/j.procs.2020.08.030


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Publication details

All authors or editors: Afsar, Bekir; Podkopaev, Dmitry; Miettinen, Kaisa

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

Parent publication editors: Cristani, Matteo; Toro, Carlos; Zanni-Merk, Cecilia; Howlett, Robert J.; Jain, Robert J.

Place and date of conference: Virtual conference, 16.-18.9.2020

Journal or series: Procedia Computer Science

ISSN: 1877-0509

eISSN: 1877-0509

Publication year: 2020

Number in series: 176

Pages range: 281-290

Number of pages in the book: 3880

Publisher: Elsevier BV

Publication country: Netherlands

Publication language: English

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

Open Access: Publication published in an open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72082


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.


Keywords: multi-objective optimisation; decision making; decision support systems; interactivity; intelligent agents

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


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Preliminary JUFO rating: 1


Last updated on 2020-09-10 at 10:34