Automata Learning Enabling Model-Based Diagnosis

Edi Muskardin, Ingo Pill, Martin Tappler, Bernhard Aichernig

    Publikation: KonferenzbeitragPapierBegutachtung

    Abstract

    The lack of a diagnostic model often prohibits us
    from deploying diagnostic reasoning for reasoning about the root causes of encountered issues.
    For overcoming this obstacle, we discuss in this
    manuscript how to exploit active automata learning for learning deterministic and stochastic models from black-box reactive systems for diagnostic purposes. On one hand, we can learn models
    of faulty systems for being able to deploy model based reasoning. Furthermore, we will also show
    how to exploit fault models in the learning process, such as to derive a behavioral model describing the entire corresponding diagnosis search
    space. In terms of applications, we will discuss
    several concrete diagnosis scenarios and how our
    models can be exploited accordingly, as well as
    report first experiments and corresponding results
    OriginalspracheEnglisch
    PublikationsstatusVeröffentlicht - 13 Sep. 2021
    Veranstaltung32nd International Workshop on Principle of Diagnosis (DX) - Hamburg, Germany
    Dauer: 13 Sep. 202115 Sep. 2021
    https://www.hsu-hh.de/imb/en/dx-2021

    Workshop

    Workshop32nd International Workshop on Principle of Diagnosis (DX)
    Zeitraum13/09/2115/09/21
    Internetadresse

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