We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which k agents strive to reach a target state, aiming to minimize the expected time to reach the target by any agent. We analyze the computational and strategy-complexity of the problem in both autonomous and coordinated settings, and we design efficient strategy-synthesis algorithms. The algorithms are experimentally evaluated on instances of increasing size against natural baselines.
@article{arxiv.2605.06056,
title = {Multiagent Stochastic Shortest Path Problem},
author = {Martin Jonáš and Antonín Kučera and Vojtěch Kůr and Jan Mačák and Vojtěch Řehák},
journal= {arXiv preprint arXiv:2605.06056},
year = {2026}
}
Comments
A full version of the paper that was presented at IJCAI 2026