TY - JOUR
T1 - Probabilistic robust multi-agent path finding
AU - Atzmon, Dor
AU - Stern, Roni
AU - Felner, Ariel
AU - Sturtevant, Nathan R.
AU - Koenig, Sven
N1 - Funding Information:
This research was supported by the Israel Ministry of Science, ISF grants #844/17 to Ariel Felner and #210/17 to Roni Stern, NSF grants 1815660 to Nathan R. Sturtevant and 1724392, 1409987, 1817189, 1837779, and 1935712 to Sven Koenig, an Amazon Research Award to Sven Koenig, and BSF grants #2017692 and #2018684.
Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2020/5/29
Y1 - 2020/5/29
N2 - In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions.
AB - In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions.
UR - http://www.scopus.com/inward/record.url?scp=85088532708&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85088532708
SN - 2334-0835
VL - 30
SP - 29
EP - 37
JO - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
JF - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
T2 - 30th International Conference on Automated Planning and Scheduling, ICAPS 2020
Y2 - 26 October 2020 through 30 October 2020
ER -