The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

中文翻译：

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使用答案集编程的具有最优资源利用的多模态多智能体路径查找的解释生成

多智能体路径查找 (MAPF) 问题是一个组合搜索问题，旨在为环境（例如，自主仓库）中的多个智能体（例如，机器人）寻找路径，使得没有两个智能体相互碰撞，并且主题对路径长度的一些限制。我们考虑一个通用版本的 MAPF，称为 mMAPF，它涉及多模式运输模式（例如，由于速度限制）和不同类型资源（例如，电池）的消耗。mMAPF 的实际应用需要灵活性（例如，解决 mMAPF 的变体）以及可解释性。我们早期对 mMAPF 的研究主要集中在灵活性的前一个挑战上。在这项研究中，我们专注于可解释性的后一个挑战，并介绍一种方法，用于为有关解决方案的可行性和最优性、解决方案的不存在以及对解决方案的观察的查询生成解释。我们的方法基于答案集编程。