Why Solving Multi-agent Path Finding with Large Language Models has not Succeeded Yet
Keywords: Large language models, multi-agent path finding, reasoning
TL;DR: In this paper, we propose our position on why even latest large language models have not succeeded in multi-agent path finding yet, and what researchers could do to address them.
Abstract: With the explosive influence caused by the success of large language models (LLM), 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 the performance of solving MAPF with LLMs. We first show the motivating success of single-agent planning and multi-agent pathfinding in an empty room map without obstacles, then the failure to plan on the harder room map and maze map of the standard MAPF benchmark. We present our position on why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis. Based on our results, we discussed how researchers with different backgrounds could help with this problem from different perspectives.
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 4760
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