Multi-agent routing problems have gained significant attention recently due to their wide range of industrial applications, ranging from logistics warehouse automation to indoor service robots. Conventionally, they are modeled as classical planning problems. In this paper, we argue that it can be beneficial to formulate them as universal planning problems, particularly when the agents are autonomous entities and may encounter unforeseen situations. We therefore propose universal plans, also known as policies, as the solution concept, and implement a system based on Answer Set Programming (ASP) to compute them. Given an arbitrary two-dimensional map and a profile of goals for a group of partially observable agents, the system translates the problem configuration into logic programs and finds a feasible universal plan for each agent, mapping its observations to actions while ensuring that there are no collisions with other agents. We use the system to conduct experiments and obtain findings regarding the types of goal profiles and environments that lead to feasible policies, as well as how feasibility may depend on the agents' sensors. We also demonstrate how users can customize action preferences to compute more efficient policies, even (near-)optimal ones. The code is available at https://github.com/Fernadoo/MAPF_ASP.

翻译：多智能体路径规划问题因其在物流仓储自动化、室内服务机器人等工业领域的广泛应用而受到广泛关注。传统上，这类问题被建模为经典规划问题。本文认为，将其表述为通用规划问题可能更具优势，尤其当智能体作为自主实体可能遭遇不可预见情况时。因此，我们提出以通用规划（亦称策略）作为解决方案，并基于回答集编程（ASP）实现了一个计算系统。给定任意二维地图和一组部分可观测智能体的目标配置，系统将问题转化为逻辑程序，并为每个智能体找到可行的通用规划，该规划将其观测映射到动作，同时确保与其他智能体无碰撞。我们通过系统实验获得了关于导致可行策略的目标配置类型与环境特征的结论，以及可行性如何受智能体传感器影响的发现。此外，我们还展示了用户如何通过定制动作偏好来计算更高效（甚至接近最优）的策略。代码发布于 https://github.com/Fernadoo/MAPF_ASP。