Studying Online Multi-Agent Path Finding

Jonathan Morag, Roni Stern, Ariel Felner, Dor Atzmon, Eli Boyarski

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Multi-agent path finding (MAPF) is the problem of planning a set of non-conflicting plans on a graph, for a set of agents. Online MAPF extends MAPF by considering a more realistic problem in which new agents may appear over time. While planning, an online solver does not know whether and which agents will join in the future. Therefore, in online problems the notion of snapshot-optimal was defined, where only current knowledge is considered. The quality of such a solution may be weaker than the quality of a solution to an equivalent offline MAPF problem (offline-optimality), where the solver is preinformed of all the agents that will appear in the future. In this paper we explore, theoretically and empirically, the quality of snapshot-optimal solutions compared to offline-optimal solutions.

Original languageEnglish
Title of host publication14th International Symposium on Combinatorial Search, SoCS 2021
EditorsHang Ma, Ivan Serina
PublisherAssociation for the Advancement of Artificial Intelligence
Pages228-230
Number of pages3
ISBN (Electronic)9781713834557
StatePublished - 2021
Externally publishedYes
Event14th International Symposium on Combinatorial Search, SoCS 2021 - Guangzhou, Virtual, China
Duration: 26 Jul 202130 Jul 2021

Publication series

Name14th International Symposium on Combinatorial Search, SoCS 2021

Conference

Conference14th International Symposium on Combinatorial Search, SoCS 2021
Country/TerritoryChina
CityGuangzhou, Virtual
Period26/07/2130/07/21

Bibliographical note

Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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