Conflict-Based Search for Explainable Multi-Agent Path Finding

TL;DR

This paper extends the Conflict-Based Search algorithm to solve explainable Multi-Agent Path Finding problems, balancing plan optimality with the need for human-understandable explanations of agent trajectories.

Contribution

It introduces a novel adaptation of CBS that incorporates explainability constraints, addressing the NP-hard challenge of generating understandable plans in MAPF.

Findings

The adapted CBS can generate explainable paths with a tradeoff between planning time and explainability.

Explainability constraints increase the complexity of MAPF, requiring specialized algorithms.

The approach demonstrates practical effectiveness in producing human-understandable multi-agent plans.

Abstract

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.