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Main Authors: Salzman, Oren, Ulloa, Carlos Hernández, Felner, Ariel, Koenig, Sven
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.25504
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author Salzman, Oren
Ulloa, Carlos Hernández
Felner, Ariel
Koenig, Sven
author_facet Salzman, Oren
Ulloa, Carlos Hernández
Felner, Ariel
Koenig, Sven
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2510_25504
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Multi-Objective Search: Algorithms, Applications, and Emerging Directions
Salzman, Oren
Ulloa, Carlos Hernández
Felner, Ariel
Koenig, Sven
Artificial Intelligence
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
title Multi-Objective Search: Algorithms, Applications, and Emerging Directions
topic Artificial Intelligence
url https://arxiv.org/abs/2510.25504