With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks. However, there is very limited work that shares insights on multi-agent planning. Multi-agent planning is different from other domains by combining the difficulty of multi-agent coordination and planning, and making it hard to leverage external tools to facilitate the reasoning needed. In this paper, we focus on the problem of multi-agent path finding (MAPF), which is also known as multi-robot route planning, and study how to solve MAPF with LLMs. We first show the motivating success on an empty room map without obstacles, then the failure to plan on a slightly harder room map. We present our hypothesis of why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis.

翻译：随着ChatGPT和GPT-4等大型语言模型（LLM）的成功带来的爆炸性影响，近期大量研究表明基础模型可用于解决多种任务。然而，关于多智能体规划领域的研究成果却极其有限。多智能体规划因其兼具多智能体协调与规划的难度，且难以借助外部工具辅助所需推理过程，而与其他领域存在显著差异。本文聚焦于多智能体路径规划（MAPF）问题（亦称多机器人路线规划），研究如何利用大语言模型解决该问题。我们首先展示了在无障碍空房间地图上的成功激励性案例，随后揭示了其在难度稍增的房间地图上规划失败的现状。我们提出关于大语言模型直接解决MAPF问题尚未成功这一现象的核心假设，并通过多项实验验证该假设。