Extracting scenarios from a Bayesian network as explanations for legal evidence

Charlotte s. Vlek, Hendrik Prakken, Silja Renooij, Bart Verheij

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


    In order to make an informed decision in a criminal trial, conclusions
    about what may have happened need to be derived from the available evidence.
    Recently, Bayesian networks have gained popularity as a probabilistic tool for rea-
    soning with evidence. However, in order to make sense of a conclusion drawn
    from a Bayesian network, a juror needs to understand the context. In this paper,
    we propose to extract scenarios from a Bayesian network to form the context for
    the results of computations in that network. We interpret the narrative concepts of
    scenario schemes, local coherence and global coherence in terms of probabilities.
    These allow us to present an algorithm that takes the most probable configuration
    of variables of interest, computed from the Bayesian network, and forms a coher-
    ent scenario as a context for these variables. This way, we take advantage of the
    calculations in a Bayesian network, as well as the global perspective of narratives
    Original languageEnglish
    Title of host publicationLegal Knowledge and Information Systems. JURIX 2014: The Twenty-seventh Annual Conference
    EditorsRinke Hoekstra
    Place of PublicationAmsterdam etc
    PublisherIOS Press
    Number of pages10
    ISBN (Electronic)978-1-61499-468-8
    ISBN (Print)978-1-61499-467-1
    Publication statusPublished - 2014

    Publication series

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


    Dive into the research topics of 'Extracting scenarios from a Bayesian network as explanations for legal evidence'. Together they form a unique fingerprint.

    Cite this