Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
This work addresses the challenge of applying LLMs to complex MAPF problems, which require planning and coordination, for researchers and practitioners in AI and robotics, though it appears incremental as it builds on existing LLM and NAR methods.
The paper tackles the problem of poor performance of large language models (LLMs) in multi-agent path finding (MAPF) tasks by proposing LLM-NAR, a framework that integrates neural algorithmic reasoners (NAR) with LLMs, resulting in significant outperformance over existing LLM-based approaches in both simulation and real-world experiments.
The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance in multi-agent path finding (MAPF) tasks has been less than satisfactory, with only a few studies exploring this area. MAPF is a complex problem requiring both planning and multi-agent coordination. To improve the performance of LLM in MAPF tasks, we propose a novel framework, LLM-NAR, which leverages neural algorithmic reasoners (NAR) to inform LLM for MAPF. LLM-NAR consists of three key components: an LLM for MAPF, a pre-trained graph neural network-based NAR, and a cross-attention mechanism. This is the first work to propose using a neural algorithmic reasoner to integrate GNNs with the map information for MAPF, thereby guiding LLM to achieve superior performance. LLM-NAR can be easily adapted to various LLM models. Both simulation and real-world experiments demonstrate that our method significantly outperforms existing LLM-based approaches in solving MAPF problems.