A4 Article in conference proceedings
Interactively Learning the Preferences of a Decision Maker in Multi-objective Optimization Utilizing Belief-rules (2020)

Misitano, G. (2020). Interactively Learning the Preferences of a Decision Maker in Multi-objective Optimization Utilizing Belief-rules. In SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (pp. 133-140). IEEE. https://doi.org/10.1109/SSCI47803.2020.9308316

JYU authors or editors

Publication details

All authors or editors: Misitano, Giovanni

Parent publication: SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence

Place and date of conference: Canberra, Australia (virtual), 1.-4.12.2020

ISBN: 978-1-7281-2547-3

Publication year: 2020

Pages range: 133-140

Number of pages in the book: 3171

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/SSCI47803.2020.9308316

Publication open access: 

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73935

Abstract

Many real life problems can be modelled as multiobjective optimization problems. Such problems often consist of multiple conflicting objectives to be optimized simultaneously. Multiple optimal solutions exist to these problems, and a single solution cannot be said to be the best without preferences given by a domain expert. Preferences can be used to find satisfying solutions: optimal solutions, which best match the expert’s preferences. To model the preferences of the expert, and aid him/her in finding satisfying solutions, a novel method is proposed. The method utilizes machine learning combined with belief-rule based systems to adaptively train a belief rule based system to learn a domain expert’s preferences using preference information gathered during an interactive process. Belief-rule based systems are explainable generalized expert systems, which have not been used before in the manner described in this paper to model preferences of a domain expert for a multi-objective optimization problem. In the case study conducted, the satisfying solutions found using learned preferences are concluded to be compatible with the preferences of the expert, which support the proposed method’s viability as a decision making support tool.

Keywords: decision support systems; optimisation; multi-objective optimisation; modelling (representation); machine learning

Free keywords: multiple objective optimization; belief-rule based systems; machine learning; Python; preference modelling; decision making

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Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1