Flexible and Explainable Solutions for Multi-Agent Path Finding Problems

TL;DR

This paper introduces flexible and explainable solutions for multi-agent path finding (MAPF), addressing the need for adaptable and understandable methods in real-world applications like autonomous warehouses.

Contribution

It presents novel approaches that enhance flexibility and explainability in MAPF solutions, accommodating variations and providing clearer reasoning.

Findings

Proposed methods improve adaptability to MAPF variations

Solutions offer better explainability for decision-making

Enhanced performance in real-world MAPF scenarios

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. The real-world applications of MAPF require flexibility (e.g., solving variations of MAPF) as well as explainability. In this study, both of these challenges are addressed and some flexible and explainable solutions for MAPF and its variants are introduced.