Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

TL;DR

This paper presents an answer set programming-based method to generate explanations for multi-modal multi-agent path finding solutions, enhancing explainability in complex, resource-constrained environments.

Contribution

It introduces a novel approach for generating explanations in mMAPF problems, addressing the previously overlooked challenge of explainability.

Findings

Provides explanations for solution feasibility and optimality

Handles nonexistence of solutions and observations about solutions

Enhances transparency in multi-modal path planning

Abstract

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.