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.
多Agent 路由问题现在引起了广泛关注，因为它们在广泛的工业应用中具有广泛的应用，例如仓库机器人、物流自动化和交通控制。传统上，它们被建模为经典规划问题。在本文中，我们认为将这些问题建模为通用规划问题有益处。因此，我们提出了称为策略的通用计划，并实现了一个名为 ASP-MAUPF(多Agent通用计划求解系统)的系统，以计算它们。给定任意二维地图和每个Agent的目标轮廓，系统找到每个Agent可行的通用计划，并确保没有与其他Agent碰撞。我们使用该系统进行了一些实验，并观察了将可行策略的目标轮廓和环境类型，以及它们可能依赖于Agent的传感器。我们还展示了用户如何自定义行动偏好以计算更高效的政策，甚至接近最优的政策。