多目标搜索框架整合复杂决策，助力机器人与交通系统优化。
Multi-Objective Search: Algorithms, Applications, and Emerging Directions
- 构建统一框架平衡多个冲突目标，适用于规划与决策问题。
- 涵盖机器人、交通等多领域应用，推动跨学科发展。
- 适合关注智能系统多目标优化的研究者与工程师。
多目标搜索（MOS）已成为规划与决策问题的统一框架，需在多个常冲突的标准间取得平衡。尽管该问题已研究数十年，近年来在人工智能应用如机器人、交通和运筹学中重获关注，反映了现实系统很少仅优化单一指标的现状。本文综述了MOS的发展，强调跨学科机遇，并指出定义其新兴前沿的开放挑战。
原文摘要 · Abstract (English)
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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