Scalable diagnosability checking of event-driven systems

Anika Schumann, Yannick Pencolé

    Research output: Contribution to journalConference articlepeer-review

    37 Citations (Scopus)

    Abstract

    Diagnosability of systems is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. Generally, in the literature of dynamic event-driven systems, diagnosability analysis is performed by algorithms that consider a system as a whole and their response is either a positive answer or a counter example. In this paper, we present an original framework for diagnosability checking. The diagnosability problem is solved in a distributed way in order to take into account the distributed nature of realistic problems. As opposed to all other approaches, our algorithm also provides an exhaustive and synthetic view of the reasons why the system is not diagnosable. Finally, the presented algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient.

    Original languageEnglish
    Pages (from-to)575-580
    Number of pages6
    JournalIJCAI International Joint Conference on Artificial Intelligence
    Publication statusPublished - 2007
    Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
    Duration: 6 Jan 200712 Jan 2007

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