Symmetry reduction for stochastic hybrid systems

L.M. Bujorianu, Joost P. Katoen

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

    56 Downloads (Pure)

    Abstract

    This paper is focused on adapting symmetry reduction, a technique that is highly successful in traditional model checking, to stochastic hybrid systems. We first show that performability analysis of stochastic hybrid systems can be reduced to a stochastic reachability analysis (SRA). Then, we generalize the notion of symmetry reduction as recently proposed for probabilistic model checking, to continuous probabilistic systems. We provide a rigorous mathematical foundation for the reduction technique in the continuous case and also investigate its observability perspective. For stochastic hybrid systems, characterizations for this reduction technique are provided, in terms of their infinitesimal generator.
    Original languageUndefined
    Title of host publication47th IEEE Conference on Decision and Control, CDC 2008
    Place of PublicationLos Alamitos
    PublisherIEEE Computer Society
    Pages233-238
    Number of pages6
    ISBN (Print)978-1-4244-3123-6
    DOIs
    Publication statusPublished - 6 Jan 2009
    Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
    Duration: 9 Dec 200811 Dec 2008
    Conference number: 47

    Publication series

    Name
    PublisherIEEE Computer Society Press
    ISSN (Print)0191-2216

    Conference

    Conference47th IEEE Conference on Decision and Control, CDC 2008
    Abbreviated titleCDC
    CountryMexico
    CityCancun
    Period9/12/0811/12/08

    Keywords

    • EWI-15275
    • abstractions
    • Reachability
    • Symmetries
    • transformation group
    • METIS-263807
    • IR-62797
    • Markov models
    • Markov Processes
    • Probability
    • probabilistic model checking

    Cite this

    Bujorianu, L. M., & Katoen, J. P. (2009). Symmetry reduction for stochastic hybrid systems. In 47th IEEE Conference on Decision and Control, CDC 2008 (pp. 233-238). [10.1109/CDC.2008.4739086] Los Alamitos: IEEE Computer Society. https://doi.org/10.1109/CDC.2008.4739086