Online Multi-Agent Path Finding: New Results.

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

doi:10.1609/socs.v15i1.21772

Online Multi-Agent Path Finding: New Results.

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

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

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

Abstract

Online MAPF extends the classical Multi-Agent Path Find ingproblem (MAPF) by considering a more realistic problem in which new agents may appear over time. As online solvers arenot aware of which agents will join in the future, the notion of snapshot-optimal was defined, where only current knowledge is considered. In this paper, we perform an extensive com-parison between oracle-optimal solutions (where the solver is preinformed of future agents), snapshot-optimal solutions, and suboptimal solutions obtained by prioritised planning

Original language	American English
Title of host publication	Proceedings of the Fifteenth International Symposium on Combinatorial Search (SoCS2022)229
Pages	229-233
Number of pages	5
DOIs	https://doi.org/10.1609/socs.v15i1.21772
State	Published - 17 Jul 2022

Keywords

Analysis Of Search Algorithms
Combinatorial optimization
Continuous Problem Solving

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10.1609/socs.v15i1.21772

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title = "Online Multi-Agent Path Finding: New Results.",

abstract = "Online MAPF extends the classical Multi-Agent Path Find ingproblem (MAPF) by considering a more realistic problem in which new agents may appear over time. As online solvers arenot aware of which agents will join in the future, the notion of snapshot-optimal was defined, where only current knowledge is considered. In this paper, we perform an extensive com-parison between oracle-optimal solutions (where the solver is preinformed of future agents), snapshot-optimal solutions, and suboptimal solutions obtained by prioritised planning",

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AU - Felner, Ariel

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AU - Atzmon, Dor

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N2 - Online MAPF extends the classical Multi-Agent Path Find ingproblem (MAPF) by considering a more realistic problem in which new agents may appear over time. As online solvers arenot aware of which agents will join in the future, the notion of snapshot-optimal was defined, where only current knowledge is considered. In this paper, we perform an extensive com-parison between oracle-optimal solutions (where the solver is preinformed of future agents), snapshot-optimal solutions, and suboptimal solutions obtained by prioritised planning

AB - Online MAPF extends the classical Multi-Agent Path Find ingproblem (MAPF) by considering a more realistic problem in which new agents may appear over time. As online solvers arenot aware of which agents will join in the future, the notion of snapshot-optimal was defined, where only current knowledge is considered. In this paper, we perform an extensive com-parison between oracle-optimal solutions (where the solver is preinformed of future agents), snapshot-optimal solutions, and suboptimal solutions obtained by prioritised planning

KW - Analysis Of Search Algorithms

KW - Combinatorial optimization

KW - Continuous Problem Solving

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DO - 10.1609/socs.v15i1.21772

M3 - Conference contribution

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BT - Proceedings of the Fifteenth International Symposium on Combinatorial Search (SoCS2022)229

ER -

Online Multi-Agent Path Finding: New Results.

Abstract

Keywords

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