Multi-Agent Path Finding with Delay Probabilities
Abstract
Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.
Cite
@article{arxiv.1612.05309,
title = {Multi-Agent Path Finding with Delay Probabilities},
author = {Hang Ma and T. K. Satish Kumar and Sven Koenig},
journal= {arXiv preprint arXiv:1612.05309},
year = {2017}
}
Comments
To appear in AAAI 2017