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Multi-agent path finding with mutex propagation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00360668" target="_blank" >RIV/68407700:21240/22:00360668 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1016/j.artint.2022.103766" target="_blank" >https://doi.org/10.1016/j.artint.2022.103766</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.artint.2022.103766" target="_blank" >10.1016/j.artint.2022.103766</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Multi-agent path finding with mutex propagation

  • Original language description

    Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in CBS, a popular optimal search-based MAPF algorithm, as well as in MDD-SAT, an optimal satisfiability-based MAPF algorithm. Mutex propagation provides CBS with the ability to break symmetries in MAPF and provides MDD-SAT with the ability to make stronger inferences than unit propagation. While existing work identifies a limited form of symmetries and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH-RCT, a state-of-the-art variant of CBS, with respect to the success rate. We also show that MDD-SAT with mutex propagation often performs better than MDD-SAT with respect to the success rate.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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/GA22-31346S" target="_blank" >GA22-31346S: logicMOVE: Logic Reasoning in Motion Planning for Multiple Robotic Agents</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

    311

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    21

  • Pages from-to

  • UT code for WoS article

    000843869600001

  • EID of the result in the Scopus database

    2-s2.0-85135327187