Multi-Objective Search: Algorithms, Applications, and Emerging Directions
It addresses the need to balance multiple objectives in real-world systems, but is incremental as a survey paper.
This paper surveys multi-objective search (MOS) as a framework for planning and decision-making with multiple conflicting criteria, highlighting its applications in AI fields like robotics and transportation, and outlines open challenges in the area.
Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, reflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS