How can graph coloring speed up multi-agent planning?
Graph Coloring to Reduce Computation Time in Prioritized Planning
This paper addresses the problem of slow computation times in multi-agent pathfinding (MAPF) when using prioritized planning (PP), a distributed approach. It proposes a new prioritization method based on graph coloring to maximize parallel computations and reduce the overall time needed to find paths for all agents.
The key point for LLM-based multi-agent systems is the idea of using a structured, decentralized approach (graph coloring) to manage dependencies between agents. This allows for increased parallelization, which is crucial for scaling LLM-based agents that can be computationally expensive. This approach could be adapted to manage dependencies between LLM agents in collaborative tasks, enabling faster overall task completion.