Scalable Mechanism Design for Multi-Agent Path Finding

TL;DR

This paper introduces scalable, strategyproof mechanisms for multi-agent path finding that work with approximate algorithms, improving overall welfare in large, realistic scenarios.

Contribution

It proposes three strategyproof mechanisms for MAPF, including two that utilize approximate algorithms, addressing incentive issues in large-scale, complex environments.

Findings

Mechanisms improve welfare over baseline methods

Two mechanisms effectively incorporate approximate MAPF algorithms

Scalable solutions work with hundreds of agents

Abstract

Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing with large numbers of agents, as is common in realistic applications like autonomous vehicle coordination. Finding an optimal solution is often computationally infeasible, making the use of approximate, suboptimal algorithms essential. Adding to the complexity, agents might act in a self-interested and strategic way, possibly misrepresenting their goals to the MAPF algorithm if it benefits them. Although the field of mechanism design offers tools to align incentives, using these tools without careful consideration can fail when only having access to approximately optimal outcomes. In this work, we introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms. We test our mechanisms on realistic MAPF domains with problem sizes ranging from dozens to hundreds of agents. We find that they improve welfare beyond a simple baseline.