Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 29-37 |
| Number of pages | 9 |
| Journal | Proceedings International Conference on Automated Planning and Scheduling, ICAPS |
| Volume | 30 |
| DOIs | |
| State | Published - 29 May 2020 |
| Externally published | Yes |
| Event | 30th International Conference on Automated Planning and Scheduling, ICAPS 2020 - Nancy, France Duration: 26 Oct 2020 → 30 Oct 2020 |
Bibliographical note
Publisher Copyright:Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding
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.
| Funders | Funder number |
|---|---|
| National Science Foundation | 1409987, 1724392, 1935712, 1837779, 1815660, 1817189 |
| Bloom's Syndrome Foundation | 2018684, 2017692 |
| Israel Science Foundation | 844/17, 210/17 |
| Ministry of science and technology, Israel |