What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization

What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization

Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers, Frans A. Oliehoek, Pradeep K. Murukannaiah

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Survey Track. Pages 6741-6749. https://doi.org/10.24963/ijcai.2023/755

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by these algorithms are scattered across fields. We provide an overview of the current advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and support on ethical aspects. We synthesize these methods drawing from different fields of research to enable building a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.
Keywords:
Survey: Agent-based and Multi-agent Systems
Survey: Constraint Satisfaction and Optimization
Survey: Multidisciplinary Topics and Applications
Survey: Humans and AI