Multi-agent routing problems have drawn significant attention nowadays due to their broad industrial applications in, e.g., warehouse robots, logistics automation, and traffic control. Conventionally, they are modelled as classical planning problems. In this paper, we argue that it is beneficial to formulate them as universal planning problems. We therefore propose universal plans, also known as policies, as the solution concepts, and implement a system called ASP-MAUPF (Answer Set Programming for Multi-Agent Universal Plan Finding) for computing them. Given an arbitrary two-dimensional map and a profile of goals for the agents, the system finds a feasible universal plan for each agent that ensures no collision with others. We use the system to conduct some experiments, and make some observations on the types of goal profiles and environments that will have feasible policies, and how they may depend on agents' sensors. We also demonstrate how users can customize action preferences to compute more efficient policies, even (near-)optimal ones.

翻译：多智能体路由问题因其在仓库机器人、物流自动化及交通控制等领域的广泛应用而受到高度关注。传统上，这些问题被建模为经典规划问题。本文提出将其表述为通用规划问题更具优势，因此我们引入通用规划（亦称策略）作为解概念，并实现名为ASP-MAUPF（基于回答集编程的多智能体通用规划求解系统）的计算系统。给定任意二维地图及智能体目标配置，该系统可为每个智能体求解确保无碰撞的可行通用规划。通过实验分析，我们观察到目标配置与环境的特定组合存在可行策略，且策略可行性可能依赖于智能体的传感器能力。此外，我们展示了用户如何通过定制动作偏好来求解更高效甚至（近）最优策略。