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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMaurice H. ter Beek, Alessandro Fantechi, Laura Semini
Place of PublicationCham
PublisherSpringer
Pages449-463
Number of pages15
ISBN (Electronic)978-3-030-30985-5
ISBN (Print)978-3-030-30984-8
DOIs
Publication statusPublished - 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11865 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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  • Cite this

    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 M. H. ter Beek, A. Fantechi, & L. Semini (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 449-463). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11865 LNCS). Springer. https://doi.org/10.1007/978-3-030-30985-5_26