发表机构
National Institute of Advanced Industrial Science and Technology (AIST)（日本国立产业技术综合研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Sokoban-LaCAM，利用多智能体路径规划的最新进展，高效解决涉及数十个智能体和箱子的多智能体推箱子问题，同时保持完备性和最终最优性保证，证明了MAPF在集体自动化中的强大作用。
AI 中文摘要
推箱子（Sokoban）是一款益智游戏，其中智能体在网格世界中推动箱子到达未标记的目标位置，它是一个长期存在的基准规划问题。尽管很容易看到其与实际应用（如配备自动叉车的仓库物流）之间的联系，但其多智能体版本仍未得到充分发展。这是因为多智能体推箱子问题由于多智能体规划特有的因素而变得困难得多，例如随着智能体数量增加而迅速增长的搜索分支因子，以及需要处理集成的任务分配和无碰撞路径规划。在本文中，我们展示了通过利用多智能体路径规划（MAPF）的最新进展，可以设计出可扩展的多智能体推箱子求解器。具体而言，我们的Sokoban-LaCAM方法能够高效地解决涉及数十个智能体和箱子的实例，同时保持完备性和最终最优性保证。这提供了证据表明，MAPF可以作为解决更广泛的集体自动化问题的强大基础工具。
英文摘要
Sokoban, a puzzle game in which an agent pushes boxes onto unlabelled target locations in a grid world, is a long-standing benchmark planning problem. While it is easy to see the connection to practical applications such as warehouse logistics with autonomous forklifts, its multi-agent counterpart has remained underdeveloped. This is because Multi-Agent Sokoban is substantially more difficult due to factors specific to multi-agent planning, such as the rapidly growing branching factor as the number of agents grows and the need to handle integrated task assignment and collision-free pathfinding. In this paper, we show that a scalable planner for Multi-Agent Sokoban can be designed by leveraging recent advances in multi-agent pathfinding (MAPF). Specifically, our Sokoban-LaCAM efficiently solves instances involving tens of agents and boxes while preserving both completeness and eventual optimality guarantees. This provides evidence that MAPF can serve as a powerful primitive for solving broader collective automation problems.
