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Generalized and sub-optimal bipartite constraints for conflict-based search

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    15 Scopus citations

    Abstract

    The main idea of conflict-based search (CBS), a popular, state-of-the-art algorithm for multi-agent pathfinding is to resolve conflicts between agents by systematically adding constraints to agents. Recently, CBS has been adapted for new domains and variants, including non-unit costs and continuous time settings. These adaptations require new types of constraints. This paper introduces a new automatic constraint generation technique called bipartite reduction (BR). BR converts the constraint generation step of CBS to a surrogate bipartite graph problem. The properties of BR guarantee completeness and optimality for CBS. Also, BR’s properties may be relaxed to obtain suboptimal solutions. Empirical results show that BR yields significant speedups in 2k connected grids over the previous state-of-the-art for both optimal and suboptimal search.

    Original languageEnglish
    Title of host publicationAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
    PublisherAAAI press
    Pages7277-7284
    Number of pages8
    ISBN (Electronic)9781577358350
    DOIs
    StatePublished - 1 Jan 2020
    Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
    Duration: 7 Feb 202012 Feb 2020

    Publication series

    NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence

    Conference

    Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
    Country/TerritoryUnited States
    CityNew York
    Period7/02/2012/02/20

    ASJC Scopus subject areas

    • Artificial Intelligence

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