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

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

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	29-37
Number of pages	9
State	Published - 2017
Externally published	Yes
Event	10th Annual Symposium on Combinatorial Search, SoCS 2017 - Pittsburgh, United States Duration: 16 Jun 2017 → 17 Jun 2017

Conference

Conference	10th Annual Symposium on Combinatorial Search, SoCS 2017
Country/Territory	United States
City	Pittsburgh
Period	16/06/17 → 17/06/17

Bibliographical note

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

ASJC Scopus subject areas

Computer Networks and Communications

Cite this

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title = "Search-based optimal solvers for the multi-agent pathfinding problem: Summary and challenges",

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.",

author = "Ariel Felner and Roni Stern and Shimony, {Solomon Eyal} and Eli Boyarski and Meir Goldenberg and Guni Sharon and Nathan Sturtevant and Glenn Wagner and Pavel Surynek",

note = "Publisher Copyright: Copyright c 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 10th Annual Symposium on Combinatorial Search, SoCS 2017 ; Conference date: 16-06-2017 Through 17-06-2017",

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language = "English",

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T2 - 10th Annual Symposium on Combinatorial Search, SoCS 2017

AU - Felner, Ariel

AU - Stern, Roni

AU - Shimony, Solomon Eyal

AU - Boyarski, Eli

AU - Goldenberg, Meir

AU - Sharon, Guni

AU - Sturtevant, Nathan

AU - Wagner, Glenn

AU - Surynek, Pavel

PY - 2017

Y1 - 2017

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AB - 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.

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M3 - Paper

AN - SCOPUS:85050542958

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Y2 - 16 June 2017 through 17 June 2017

ER -

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

Abstract

Conference

Bibliographical note

ASJC Scopus subject areas

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Cite this