Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities

TL;DR

This paper surveys compilation-based methods for multi-agent path finding, comparing different formalisms like ASP, MIP, and SAT, and discusses their advantages, challenges, and future research directions.

Contribution

It provides a comprehensive overview of current compilation-based MAPF solvers, highlighting lessons learned and future opportunities in the field.

Findings

Compilation-based MAPF solvers leverage advances in formal methods.

Different formalisms offer unique benefits and challenges.

Future research can improve scalability and efficiency.

Abstract

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions. The combination of two additional requirements makes the problem computationally challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. Two major approaches to optimal MAPF solving include (1) dedicated search-based methods, which solve MAPF directly, and (2) compilation-based methods that reduce a MAPF instance to an instance in a different well established formalism, for which an efficient solver exists. The compilation-based MAPF solving can benefit from advancements accumulated during the development of the target solver often decades long. We summarize and compare contemporary compilation-based solvers for MAPF using formalisms like ASP, MIP, and SAT. We show the lessons learned from past developments and current trends in the topic and discuss its wider impact.