TL;DR
This paper surveys recent advances in multi-objective search (MOS), emphasizing its growing importance across AI fields, highlighting new applications, and discussing open challenges for future research.
Contribution
It provides a comprehensive overview of MOS algorithms, applications, and emerging directions, identifying cross-disciplinary opportunities and open challenges.
Findings
MOS is increasingly applied in robotics, transportation, and operations research.
Recent developments have expanded the scope and effectiveness of MOS algorithms.
Open challenges include scalability and integration of diverse criteria.
Abstract
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
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