Data-driven Interactive Multiobjective Optimization : Challenges and a Generic Multi-agent Architecture
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
Published inProcedia Computer Science
© 2020 The Author(s). Published by Elsevier B.V.
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.
ConferenceInternational Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Is part of publicationKES 2020 : Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Research profiles, AoF
Additional information about fundingThis 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.
Showing items with similar title or keywords.
Liu, Shenghua (University of Jyväskylä, 2010)
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