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Non-Refined Abstractions in Counterexample Guided Abstraction Refinement for Multi-Agent Path Finding

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00371963" target="_blank" >RIV/68407700:21240/23:00371963 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICTAI59109.2023.00055" target="_blank" >https://doi.org/10.1109/ICTAI59109.2023.00055</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICTAI59109.2023.00055" target="_blank" >10.1109/ICTAI59109.2023.00055</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Non-Refined Abstractions in Counterexample Guided Abstraction Refinement for Multi-Agent Path Finding

  • Original language description

    Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that the agents do not collide with each other.The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process making the SAT-based solver for MAPF competitive again in relevant benchmarks.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • 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

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    35th IEEE International Conference on Tools with Artificial Intelligence

  • ISBN

    979-8-3503-4273-4

  • ISSN

    1082-3409

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    333-340

  • Publisher name

    IEEE Computer Society

  • Place of publication

    Cannes

  • Event location

    Atlanta

  • Event date

    Nov 6, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001139095400047