Combining binary constraint networks in qualitative reasoning

Jason Jingshi Li, Tomasz Kowalski, Jochen Renz, Sanjiang Li

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

    3 Citations (Scopus)

    Abstract

    Constraint networks in qualitative spatial and temporal reasoning are always complete graphs. When one adds an extra element to a given network, previously unknown constraints are derived by intersections and compositions of other constraints, and this may introduce inconsistency to the overall network. Likewise, when combining two consistent networks that share a common part, the combined network may become inconsistent. In this paper, we analyse the problem of combining these binary constraint networks and develop certain conditions to ensure combining two networks will never introduce an inconsistency for a given spatial or temporal calculus. This enables us to maintain a consistent world-view while acquiring new information in relation with some part of it. In addition, our results enable us to prove other important properties of qualitative spatial and temporal calculi in areas such as representability and complexity.

    Original languageEnglish
    Title of host publicationFrontiers in Artificial Intelligence and Applications
    PublisherIOS Press BV
    Pages515-519
    Number of pages5
    ISBN (Print)978158603891
    DOIs
    Publication statusPublished - 2018
    Event18th European Conference on Artificial Intelligence, ECAI 2008 - Patras, Greece
    Duration: 21 Jul 200825 Jul 2008

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    Volume178
    ISSN (Print)0922-6389
    ISSN (Electronic)1879-8314

    Conference

    Conference18th European Conference on Artificial Intelligence, ECAI 2008
    Country/TerritoryGreece
    CityPatras
    Period21/07/0825/07/08

    Fingerprint

    Dive into the research topics of 'Combining binary constraint networks in qualitative reasoning'. Together they form a unique fingerprint.

    Cite this