Heuristically Guided Compilation for Multi-Agent Path Finding

TL;DR

This paper introduces a heuristic-guided compilation approach for multi-agent path finding that improves SAT-based solver performance by incorporating domain-specific heuristics into the encoding process.

Contribution

It presents a novel method to embed heuristics into MAPF compilation for SAT solvers, enhancing efficiency over standard approaches.

Findings

Heuristically guided compilation outperforms vanilla SAT-based MAPF solvers.

Encoding based on candidate paths reduces complexity and improves solution quality.

Experimental results demonstrate significant performance gains.

Abstract

Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MILP), Boolean satisfiability (SAT), or constraint programming (CP). As the target solvers for these formalisms act as black-boxes it is challenging to integrate MAPF specific heuristics in the MAPF compilation-based solvers. We show in this work how the build a MAPF encoding for the target SAT solver in which domain specific heuristic knowledge is reflected. The heuristic knowledge is transferred to the SAT solver by selecting candidate paths for each agent and by constructing the encoding only for these candidate paths instead of constructing the encoding for all possible paths for an agent. The conducted experiments show that heuristically guided compilation outperforms the vanilla variants of the SAT-based MAPF solver.