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An Axiomatization of Strong Distribution Bisimulation for a Language with a Parallel Operator and Probabilistic Choice

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Abstract

In the setting of a simple process language featuring non-deterministic choice and a parallel operator on the one hand and probabilistic choice on the other hand, we propose an axiomatization capturing strong distribution bisimulation. Contrary to other process equivalences for probabilistic process languages, in this paper distributions rather than states are the leading ingredients for building the semantics and the accompanying equational theory, for which we establish soundness and completeness.

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Acknowledgement

JFG acknowledges the mutual inspiration of the development teams of mCRL2 and KandISTI. A nice example is the inclusion of the LTS minimization algorithm as provided in the mCRL2 toolset which has been incorporated in the KandISTI family members UMC and FMC. Also the attention for model checking of variability and software product lines with the mCRL2 toolset is such an example. EV acknowledges the warm hospitality of Stefania Gnesi and her research group at the CNR in Pisa at various occasions and the many pasti accoglienti shared together.

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Groote, J.F., de Vink, E.P. (2019). An Axiomatization of Strong Distribution Bisimulation for a Language with a Parallel Operator and Probabilistic Choice. In: ter Beek, M., Fantechi, A., Semini, L. (eds) From Software Engineering to Formal Methods and Tools, and Back. Lecture Notes in Computer Science(), vol 11865. Springer, Cham. https://doi.org/10.1007/978-3-030-30985-5_26

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  • DOI: https://doi.org/10.1007/978-3-030-30985-5_26

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