Solving Multi-Agent Pathfinding with Stochastic Local Search SAT Algorithms

Max Frommknecht, Pavel Surynek

2024

Abstract

This paper explores the suitability of Stochastic Local Search (SLS) solvers for Multi-Agent Pathfinding (MAPF) translated into the SAT domain. Traditionally, SAT encodings of MAPF have been tackled using Conflict-Driven Clause Learning (CDCL) solvers, but this work investigates the potential of SLS solvers, particularly ProbSAT, in solving MAPF under the makespan objective. By employing the MDD-SAT approach and comparing the performance of ProbSAT against the Glucose 4 CDCL solver, the effects of eager and lazy encodings are evaluated, as well as the benefit of providing initial variable assignments. Results show that ProbSAT can effectively solve MAPF instances, especially when initial assignments based on agents’ shortest paths are provided. This study suggests that SLS solvers can compete with CDCL solvers in specific MAPF scenarios and highlights future research directions for optimizing SLS performance in MAPF.

Paper Citation

in Harvard Style

Frommknecht M. and Surynek P. (2024). Solving Multi-Agent Pathfinding with Stochastic Local Search SAT Algorithms. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 67-78. DOI: 10.5220/0012944800003822

in Bibtex Style

@conference{icinco24,
author={Max Frommknecht and Pavel Surynek},
title={Solving Multi-Agent Pathfinding with Stochastic Local Search SAT Algorithms},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={67-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012944800003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Solving Multi-Agent Pathfinding with Stochastic Local Search SAT Algorithms
SN - 978-989-758-717-7
AU - Frommknecht M.
AU - Surynek P.
PY - 2024
SP - 67
EP - 78
DO - 10.5220/0012944800003822
PB - SciTePress