A modal characterization theorem for a probabilistic fuzzy description logic

Paul Wild, Lutz Schröder, Dirk Pattinson, Barbara König

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

    3 Citations (Scopus)

    Abstract

    The fuzzy modality probably is interpreted over probabilistic type spaces by taking expected truth values. The arising probabilistic fuzzy description logic is invariant under probabilistic bisimilarity; more informatively, it is non-expansive wrt. a suitable notion of behavioural distance. In the present paper, we provide a characterization of the expressive power of this logic based on this observation: We prove a probabilistic analogue of the classical van Benthem theorem, which states that modal logic is precisely the bisimulation-invariant fragment of first-order logic. Specifically, we show that every formula in probabilistic fuzzy first-order logic that is non-expansive wrt. behavioural distance can be approximated by concepts of bounded rank in probabilistic fuzzy description logic.

    Original languageEnglish
    Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    EditorsSarit Kraus
    PublisherInternational Joint Conferences on Artificial Intelligence
    Pages1900-1906
    Number of pages7
    ISBN (Electronic)9780999241141
    DOIs
    Publication statusPublished - 2019
    Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
    Duration: 10 Aug 201916 Aug 2019

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    Volume2019-August
    ISSN (Print)1045-0823

    Conference

    Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    Country/TerritoryChina
    CityMacao
    Period10/08/1916/08/19

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