How to shorten multi-agent paths on graphs?
Dynamic Programming based Local Search approaches for Multi-Agent Path Finding problems on Directed Graphs
This paper tackles the Multi-Agent Path Finding (MAPF) problem on directed graphs, focusing on optimizing suboptimal solutions for better efficiency. It introduces a local search procedure using dynamic programming to improve existing solutions by exploring nearby, potentially shorter paths.
For LLM-based multi-agent systems, the key takeaway is the concept of iteratively refining suboptimal solutions using local search. This can be valuable when deploying LLMs as agents in complex environments where optimal solutions are computationally expensive or infeasible to obtain. The paper's focus on directed graphs and polynomial time complexity offers practical relevance for real-world web applications where agent interactions often resemble directed graph structures.