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

翻译：多目标搜索（MOS）已成为一个统一的框架，用于处理需要平衡多个（通常相互冲突的）准则的规划与决策问题。尽管该问题已被研究数十年，近年来在人工智能应用（如机器人学、交通运输和运筹学）中对该主题的兴趣重新兴起，这反映了现实世界系统很少仅优化单一指标的实际情况。本文综述了MOS的发展，同时强调了跨学科机遇，并概述了定义MOS新兴前沿的开放挑战。