Probabilistic robust multi-agent path finding

Dor Atzmon, Roni Stern, Ariel Felner, Nathan R. Sturtevant, Sven Koenig

Research output: Contribution to journalConference articlepeer-review

24 Scopus citations


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 languageEnglish
Pages (from-to)29-37
Number of pages9
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
StatePublished - 29 May 2020
Event30th International Conference on Automated Planning and Scheduling, ICAPS 2020 - Nancy, France
Duration: 26 Oct 202030 Oct 2020

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management


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