经典多智能体路径规划的解质量代价
原标题：The Cost of Classical Multi-Agent Path Finding
AI 摘要
该论文研究经典多智能体路径规划（MAPF）因离散时间、单位边长和顶点/边冲突假设而牺牲的解质量。作者以连续时间MAPF（MAPFR）作为对照，在不同智能体数量、尺寸、图连通性、拓扑和分辨率下比较，发现连续时间和智能体形状本身价值有限，其价值主要来自扩展移动动作范围，在狭窄受限地图和开放空间地图上平均提升解质量，部分情况提升显著。将经典MAPF地图分辨率加倍只能恢复不到部分损失，说明更多算力难以弥补该公式的固有局限。
正文节选
The Cost of Classical Multi-Agent Path Finding Abstract. Multi-Agent Path Finding (MAPF) is the problem of planning conflict-free paths for multiple agents in a shared space, each from its start to its goal. Classical MAPF has been the dominant formulation for many years, with its assumptions of discrete time and graph-based conflicts presumably easing the search for solutions. These assumptions limit the physical environments and agents for which a solution is truly collision-free, and also pla