How can I improve multi-agent pathfinding efficiency?
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
This paper tackles the challenge of efficient multi-agent pathfinding (MAPF) by proposing a method to compute multiple agent prioritizations simultaneously. Traditional prioritized planning (PP) assigns priorities to agents (e.g., agent 1 moves before agent 2), but the quality and even existence of solutions heavily depends on this priority order. Testing all possible priority orders is computationally expensive. This work develops a novel approach to compute multiple prioritizations concurrently, improving solution quality without significantly increasing computation time. It uses a "computation schedule matrix" similar to a Sudoku puzzle to ensure each agent works on a different prioritization within a given time slot.
For LLM-based multi-agent systems, this research is relevant for scenarios requiring concurrent execution of multiple agents with dependencies. The proposed method offers a way to explore different execution orders (prioritizations) more efficiently than sequential testing, leading to better coordination and outcomes. This is particularly valuable when agent interactions are complex, mimicking real-world scenarios where determining the optimal interaction order is crucial. The concept of parallelizing agent computations is relevant for managing multiple LLM agents with limited resources.