Synthesis for PCTL in Parametric Markov Decision Processes

Ernst Moritz Hahn, Tingting Han, Lijun Zhang

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    In parametric Markov decision processes (PMDPs), transition probabilities are not fixed, but are given as functions over a set of parameters. A PMDP denotes a family of concrete MDPs. This paper studies the synthesis problem for PCTL in PMDPs: Given a specification Φ in PCTL, we synthesise the parameter valuations under which Φ is true. First, we divide the possible parameter space into hyper-rectangles. We use existing decision procedures to check whether Φ holds on each of the Markov processes represented by the hyper-rectangle. As it is normally impossible to cover the whole parameter space by hyper-rectangles, we allow a limited area to remain undecided. We also consider an extension of PCTL with reachability rewards. To demonstrate the applicability of the approach, we apply our technique on a case study, using a preliminary implementation.
    Original languageEnglish
    Title of host publicationNASA Formal Methods : Third International Symposium, NFM 2011 - Pasadena, CA, USA, April 18-20, 2011 - Proceedings
    Publication date2011
    ISBN (Print)978-3-642-20397-8
    Publication statusPublished - 2011
    EventNASA Formal Methods Symposium - Pasadena, California, USA
    Duration: 1 Jan 2011 → …
    Conference number: 3


    ConferenceNASA Formal Methods Symposium
    CityPasadena, California, USA
    Period01/01/2011 → …
    SeriesLecture Notes in Computer Science


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