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
@article{arxiv.2510.25504,
title = {Multi-Objective Search: Algorithms, Applications, and Emerging Directions},
author = {Oren Salzman and Carlos Hernández Ulloa and Ariel Felner and Sven Koenig},
journal= {arXiv preprint arXiv:2510.25504},
year = {2025}
}