Multi-agent pathfinding with continuous time
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00357053" target="_blank" >RIV/68407700:21240/22:00357053 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.artint.2022.103662" target="_blank" >https://doi.org/10.1016/j.artint.2022.103662</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.artint.2022.103662" target="_blank" >10.1016/j.artint.2022.103662</a>
Alternative languages
Result language
angličtina
Original language name
Multi-agent pathfinding with continuous time
Original language description
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that each agent reaches its goal and the agents do not collide. In recent years, variants of MAPF have risen in a wide range of real-world applications such as warehouse management and autonomous vehicles. Optimizing common MAPF objectives, such as minimizing sum-of-costs or makespan, is computationally intractable, but state-of-the-art algorithms are able to solve optimally problems with dozens of agents. However, most MAPF algorithms assume that (1) time is discretized into time steps and (2) the duration of every action is one time step. These simplifying assumptions limit the applicability of MAPF algorithms in real-world applications and raise non-trivial questions such as how to discretize time in an effective manner. We propose two novel MAPF algorithms for finding optimal solutions that do not rely on any time discretization.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA19-17966S" target="_blank" >GA19-17966S: intALG-MAPFg: Intelligent Algorithms for Generalized Variants of Multi-Agent Path Finding</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence
ISSN
0004-3702
e-ISSN
1872-7921
Volume of the periodical
2022
Issue of the periodical within the volume
305
Country of publishing house
GB - UNITED KINGDOM
Number of pages
32
Pages from-to
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UT code for WoS article
000767667600009
EID of the result in the Scopus database
2-s2.0-85125812849