A Competitive Analysis of Online Multi-Agent Path Finding

TL;DR

This paper analyzes online Multi-Agent Path Finding (MAPF), providing complexity classifications, competitive ratios, and theoretical bounds for various algorithm categories in dynamic, real-time scenarios.

Contribution

It generalizes offline MAPF complexity results to online settings and introduces a classification framework for online MAPF algorithms based on controllability and rationality.

Findings

Naive sequential routing has competitive ratio bounded by the number of agents.

Without rerouting, all rational algorithms share the same asymptotic competitive ratio as naive methods.

Rerouting allows for constant lower bounds on competitive ratios, highlighting the importance of rerouting in online MAPF.

Abstract

We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to commonly-used objective functions. We show that a naive algorithm that routes newly-revealed agents one at a time in sequence achieves a competitive ratio that is asymptotically bounded from both below and above by the number of agents with respect to flowtime and makespan. We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on the competitive ratio of any rational online MAPF algorithms that allow rerouting. The results thus provide theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time.