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Probabilistic Bisimulation for Realistic Schedulers

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FM 2015: Formal Methods (FM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9109))

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Abstract

Weak distribution bisimilarity is an equivalence notion on probabilistic automata, originally proposed for Markov automata. It has gained some popularity as the coarsest behavioral equivalence enjoying valuable properties like preservation of trace distribution equivalence and compositionality. This holds in the classical context of arbitrary schedulers, but it has been argued that this class of schedulers is unrealistically powerful. This paper studies a strictly coarser notion of bisimilarity, which still enjoys these properties in the context of realistic subclasses of schedulers: Trace distribution equivalence is implied for partial information schedulers, and compositionality is preserved by distributed schedulers. The intersection of the two scheduler classes thus spans a coarser and still reasonable compositional theory of behavioral semantics.

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Author information

Authors and Affiliations

  1. Saarland University, Saarbrücken, Germany

    Christian Eisentraut & Holger Hermanns

  2. IT University of Copenhagen, København S, Denmark

    Jens Chr. Godskesen

  3. University of Technology Sydney, Sydney, Australia

    Lei Song

  4. State Key Laboratory of Computer Science, Institute of Software, CAS, Beijing, China

    Lijun Zhang

Authors
  1. Christian Eisentraut
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  2. Jens Chr. Godskesen
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  3. Holger Hermanns
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  4. Lei Song
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  5. Lijun Zhang
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Editor information

Editors and Affiliations

  1. Microsoft Research, Redmond, Washington, USA

    Nikolaj Bjørner

  2. Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands

    Frank de Boer

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Eisentraut, C., Godskesen, J.C., Hermanns, H., Song, L., Zhang, L. (2015). Probabilistic Bisimulation for Realistic Schedulers. In: Bjørner, N., de Boer, F. (eds) FM 2015: Formal Methods. FM 2015. Lecture Notes in Computer Science(), vol 9109. Springer, Cham. https://doi.org/10.1007/978-3-319-19249-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-19249-9_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19248-2

  • Online ISBN: 978-3-319-19249-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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