Towards explainable interactive multiobjective optimization : R-XIMO
Misitano, G., Afsar, B., Lárraga, G., & Miettinen, K. (2022). Towards explainable interactive multiobjective optimization : R-XIMO. Autonomous Agents and Multi-Agent Systems, 36(2), Article 43. https://doi.org/10.1007/s10458-022-09577-3
Published inAutonomous Agents and Multi-Agent Systems
DisciplineLaskennallinen tiedeMultiobjective Optimization GroupComputational ScienceMultiobjective Optimization Group
© The Author(s) 2022
In interactive multiobjective optimization methods, the preferences of a decision maker are incorporated in a solution process to find solutions of interest for problems with multiple conflicting objectives. Since multiple solutions exist for these problems with various trade-offs, preferences are crucial to identify the best solution(s). However, it is not necessarily clear to the decision maker how the preferences lead to particular solutions and, by introducing explanations to interactive multiobjective optimization methods, we promote a novel paradigm of explainable interactive multiobjective optimization. As a proof of concept, we introduce a new method, R-XIMO, which provides explanations to a decision maker for reference point based interactive methods. We utilize concepts of explainable artificial intelligence and SHAP (Shapley Additive exPlanations) values. R-XIMO allows the decision maker to learn about the trade-offs in the underlying problem and promotes confidence in the solutions found. In particular, R-XIMO supports the decision maker in expressing new preferences that help them improve a desired objective by suggesting another objective to be impaired. This kind of support has been lacking. We validate R-XIMO numerically, with an illustrative example, and with a case study demonstrating how R-XIMO can support a real decision maker. Our results show that R-XIMO successfully generates sound explanations. Thus, incorporating explainability in interactive methods appears to be a very promising and exciting new research area. ...
PublisherSpringer Science and Business Media LLC
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Research profiles, AoF
Additional information about fundingThis work has been supported by the Academy of Finland (Grant Numbers 311877 and 322221) and the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. This work is a part of the thematic research area Decision Analytics Utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) at the University of Jyväskylä. Open Access funding provided by University of Jyväskylä (JYU). ...
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