In the Multi-Agent Path Finding (MAPF) problem, the goal is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. To this end, a recent work introduces a notion of explainability for MAPF based on a visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint. Then, the explainable MAPF problem asks for a set of non-colliding paths that admits a short-enough explanation. Explainable MAPF adds a new difficulty to MAPF, in that it is NP-hard with respect to the size of the environment, and not just the number of agents. Thus, traditional MAPF algorithms are not equipped to directly handle explainable-MAPF. In this work, we adapt Conflict Based Search (CBS), a well-studied algorithm for MAPF, to handle explainable MAPF. We show how to add explainability constraints on top of the standard CBS tree and its underlying A* search. We examine the usefulness of this approach and, in particular, the tradeoff between planning time and explainability.

翻译：在多代理路径查找(MAPF)问题中,目标是为环境中的代理物找到非对称路径,使每个代理物都能从最初的位置到达其目标。在安全关键应用中,一位人体主管可能希望核实该计划是否确实没有碰撞。为此,最近的一项工作为MAPF提出了一个解释性概念,其依据是将计划作为代表时间段的短顺序的图像的可视化过程,在每一个时间段,代理物的轨迹是脱节的。然后,可解释性MAPF问题要求提供一套非对称路径,以承认一个短时间的解释。可解释的MAPFF给MAPF增加了新的困难,因为它在环境大小方面是硬性NP,而不仅仅是代理物的数量。因此,传统的MAPF算法无法直接处理可解释的时段段。在这项工作中,我们调整了基于冲突的搜索(CBS)的算法,这是对MAPFPF的一种经过很好研究的算法,可以处理可解释性解释性解释性的方法。我们展示了如何解释这一标准CBS树顶端和顶端之间的可操作性。