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.

翻译：多智能体路径规划涉及为多个智能体同时规划无碰撞路径，使其在共享区域内从起点到达指定目标位置。该问题在计算上具有高度复杂性，尤其在处理大量智能体时更为显著——这正是自主车辆协调等实际应用中的常见场景。寻找最优解往往在计算上不可行，因此使用近似次优算法至关重要。更复杂的是，智能体可能以自利且策略性的方式行动，若对自身有利，它们可能向路径规划算法虚报目标。尽管机制设计领域提供了激励相容的工具，但在仅能获得近似最优解的情况下，贸然使用这些工具可能导致失效。本文提出可扩展的多智能体路径规划机制设计问题，并设计了三种策略proof机制，其中两种甚至能兼容近似路径规划算法。我们在包含数十至数百个智能体的实际路径规划场景中测试了所提机制，结果表明这些机制能够提升社会福利，显著优于简单基线方法。