论文标题
使用答案集编程的最佳资源利用率的多模式多模式多代理路径查找的说明生成
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
论文作者
论文摘要
多试路径查找（MAPF）问题是一个组合搜索问题，旨在在环境（例如自主仓库）中找到多种代理（例如机器人）的路径，因此没有两个代理相互碰撞，并在路径长度上受到某些约束。我们考虑了一个称为MMAPF的通用版本，该版本涉及多模式运输模式（例如，由于速度限制）和不同类型的资源（例如电池）的消耗。 MMAPF的现实应用需要灵活性（例如，解决MMAPF的变化）以及解释性。我们先前对MMAPF的研究集中在以前的灵活性挑战上。在这项研究中，我们着重于解释性的后一种挑战，并引入了一种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有关解决方案的观察的解释的方法。我们的方法基于答案集编程。本文正在考虑在TPLP中接受。
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. This paper is under consideration for acceptance in TPLP.
