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Uniform Random Process Model Revisited

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Abstract

Recently, a proper bisimulation equivalence relation for random process model has been defined in a model independent approach. Model independence clarifies the difference between nondeterministic and probabilistic actions in concurrency and makes the new equivalence relation to be congruent. In this paper, we focus on the finite state randomized \(\text {CCS} \) model and deepen the previous work in two aspects. First, we show that the equivalence relation can be decided in polynomial time. Second, we give a sound and complete axiomatization system for this model. The algorithm and axiomatization system also have the merit of model independency as they can be easily generalized to the randomized extension of any finite state concurrent model.

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Acknowledgement

We are grateful to Prof. Yuxi Fu for his instructive discussions and feedbacks. We thank Dr. Mingzhang Huang, Dr. Qiang Yin and other members of BASICS for offering helps in the revision stage. We also thank the anonymous referees for their questions and detailed comments. The support from the National Science Foundation of China (61772336, 61872142, 61572318) is acknowledged.

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Authors and Affiliations

  1. BASICS, Shanghai Jiao Tong University, Shanghai, China

    Wenbo Zhang & Huan Long

  2. East China University of Science and Technology, Shanghai, China

    Xian Xu

Authors
  1. Wenbo Zhang
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  2. Huan Long
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  3. Xian Xu
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Corresponding authors

Correspondence to Wenbo Zhang , Huan Long or Xian Xu .

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Editors and Affiliations

  1. University of Kaiserslautern, Kaiserslautern, Germany

    Anthony Widjaja Lin

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Zhang, W., Long, H., Xu, X. (2019). Uniform Random Process Model Revisited. In: Lin, A. (eds) Programming Languages and Systems. APLAS 2019. Lecture Notes in Computer Science(), vol 11893. Springer, Cham. https://doi.org/10.1007/978-3-030-34175-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-34175-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34174-9

  • Online ISBN: 978-3-030-34175-6

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