Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem

Max Barer; Guni Sharon; Roni Stern; Ariel Felner

doi:10.1609/socs.v5i1.18315

Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem

Max Barer, Guni Sharon, Roni Stern, Ariel Felner

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

232 Scopus citations

Abstract

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.

Original language	English
Title of host publication	Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Editors	Stefan Edelkamp, Roman Bartak
Publisher	Association for the Advancement of Artificial Intelligence
Pages	19-27
Number of pages	9
ISBN (Electronic)	9781577356769
DOIs	https://doi.org/10.1609/socs.v5i1.18315
State	Published - 2014
Externally published	Yes
Event	7th Annual Symposium on Combinatorial Search, SoCS 2014 - Prague, Czech Republic Duration: 15 Aug 2014 → 17 Aug 2014

Publication series

Name	Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Volume	2014-January

Conference

Conference	7th Annual Symposium on Combinatorial Search, SoCS 2014
Country/Territory	Czech Republic
City	Prague
Period	15/08/14 → 17/08/14

Bibliographical note

Publisher Copyright:
Copyright © 2014.

Funding

This research was supported by the Israel Science Foundation (ISF) under grant #417/13 to Ariel Felner.

Funders	Funder number
Israel Science Foundation	417/13

Access to Document

10.1609/socs.v5i1.18315

Cite this

Barer, M., Sharon, G., Stern, R., & Felner, A. (2014). Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. In S. Edelkamp, & R. Bartak (Eds.), Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014 (pp. 19-27). (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014; Vol. 2014-January). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/socs.v5i1.18315

Barer, Max ; Sharon, Guni ; Stern, Roni et al. / Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. editor / Stefan Edelkamp ; Roman Bartak. Association for the Advancement of Artificial Intelligence, 2014. pp. 19-27 (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014).

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title = "Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem",

abstract = "The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.",

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Barer, M, Sharon, G, Stern, R & Felner, A 2014, Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. in S Edelkamp & R Bartak (eds), Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014, vol. 2014-January, Association for the Advancement of Artificial Intelligence, pp. 19-27, 7th Annual Symposium on Combinatorial Search, SoCS 2014, Prague, Czech Republic, 15/08/14. https://doi.org/10.1609/socs.v5i1.18315

Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. / Barer, Max; Sharon, Guni; Stern, Roni et al.
Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. ed. / Stefan Edelkamp; Roman Bartak. Association for the Advancement of Artificial Intelligence, 2014. p. 19-27 (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014; Vol. 2014-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Barer M, Sharon G, Stern R, Felner A. Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. In Edelkamp S, Bartak R, editors, Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. Association for the Advancement of Artificial Intelligence. 2014. p. 19-27. (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014). doi: 10.1609/socs.v5i1.18315

Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem

Abstract

Publication series

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Bibliographical note

Funding

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Cite this