How can I improve multi-agent pathfinding with limited communication?
Multi-Agent Path Finding under Limited Communication Range Constraint via Dynamic Leading
This paper addresses the problem of coordinating multiple agents navigating a space while maintaining communication links, a crucial aspect of real-world multi-agent systems. The proposed MA-DL framework uses dynamic leader selection to overcome limitations of fixed-leader approaches, enabling more robust planning. This dynamic adaptation is relevant to LLM-based multi-agent systems, as it provides a mechanism for agents to adjust their roles and strategies based on the situation, potentially improving communication effectiveness and overall system performance. The focus on maintaining communication constraints is also highly relevant, as effective communication is essential for collaborative task completion in LLM-based multi-agent applications.