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Search-based optimal solvers for the multi-agent pathfinding problem: Summary and challenges

  • Ariel Felner
  • , Roni Stern
  • , Solomon Eyal Shimony
  • , Eli Boyarski
  • , Meir Goldenberg
  • , Guni Sharon
  • , Nathan Sturtevant
  • , Glenn Wagner
  • , Pavel Surynek
  • Ben-Gurion University of the Negev
  • Jerusalem College of Technology
  • University of Texas at Austin
  • University of Denver
  • Carnegie Mellon University
  • National Institute of Advanced Industrial Science and Technology

Research output: Contribution to conferencePaperpeer-review

180 Scopus citations

Abstract

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this research area, numerous diverse search-based techniques were developed in the past 6 years for optimally solving MAPF under the sum-of-costs objective function. In this paper we survey these techniques, while placing them into the wider context of the MAPF field of research. Finally, we provide analytical and experimental comparisons that show that no algorithm dominates all others in all circumstances. We conclude by listing important future research directions.

Original languageEnglish
Pages29-37
Number of pages9
StatePublished - 2017
Event10th Annual Symposium on Combinatorial Search, SoCS 2017 - Pittsburgh, United States
Duration: 16 Jun 201717 Jun 2017

Conference

Conference10th Annual Symposium on Combinatorial Search, SoCS 2017
Country/TerritoryUnited States
CityPittsburgh
Period16/06/1717/06/17

Bibliographical note

Publisher Copyright:
Copyright c 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This research was supported by Israel Science Foundation (ISF) grant #417/13 and by a joint grant of the Israel and Czech Ministries of Science #8G15027.

FundersFunder number
Israel and Czech Ministries of Science8G15027
Israel Science Foundation417/13

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