Can LLMs solve MAPF deadlocks?
LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding
This paper introduces LLMDR, a system that uses Large Language Models (LLMs) to help multi-agent pathfinding systems avoid and escape deadlocks. LLMDR works by analyzing the planned movements of agents, identifying potential deadlocks using the LLM, and then using LLM-generated strategies along with a prioritized planning algorithm (PIBT) to adjust agent actions and priorities to resolve the deadlock. Experiments showed LLMDR improved the performance of existing multi-agent pathfinding methods, especially in complex scenarios. Key for LLM-based multi-agent systems is the demonstration of using LLMs for high-level reasoning (deadlock detection and strategy generation) combined with a more traditional algorithm for low-level action planning, addressing some limitations of using LLMs for the entire task.