A4 Article in conference proceedings
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 authors or editors
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: https://doi.org/10.1016/j.procs.2020.08.030
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72082
Abstract
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
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 3
- Hämäläinen, Keijo
- Research Council of Finland
- Data-driven Decision Support with Multiobjective Optimization (DAEMON)
- Miettinen, Kaisa
- Research Council of Finland
Ministry reporting: Yes
VIRTA submission year: 2020
JUFO rating: 1