On Inductive Characterization for Divergence-sensitive Probabilistic Branching Bisimilarity

Recently a divergence-sensitive branching bisimilarity has been proposed and studied for the randomized CCS model. In this paper, we give an equivalent inductive characterization for the bisimilarity, which is a probabilistic extension of the previous work on the non-probabilistic model. Based on the new characterization, a novel polynomial-time verification algorithm for the divergence-sensitive branching bisimilarity is proposed.

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Publication Details

Journal
Formal Aspects of Computing
Published
2026-03-23
DOI
https://doi.org/10.1145/3803544
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
0.00
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article

On Inductive Characterization for Divergence-sensitive Probabilistic Branching Bisimilarity

Yuxi Fu, Huan Long, Wenbo Zhang, Hao Wu et al.
Formal Aspects of Computing
Markov Chains and Monte Carlo Methods
article

On Inductive Characterization for Divergence-sensitive Probabilistic Branching Bisimilarity

Yuxi Fu, Huan Long, Wenbo Zhang, Hao Wu, Xian Xu
article en

Abstract

Recently a divergence-sensitive branching bisimilarity has been proposed and studied for the randomized CCS model. In this paper, we give an equivalent inductive characterization for the bisimilarity, which is a probabilistic extension of the previous work on the non-probabilistic model. Based on the new characterization, a novel polynomial-time verification algorithm for the divergence-sensitive branching bisimilarity is proposed.

Formal Aspects of Computing
East China University of Science and Technology (CN), Shanghai Jiao Tong University (CN), British Association for Immediate Care Scotland (GB), BASilar artery International Cooperation Study (NL), Shanghai Ocean University (CN), Shanghai Maritime University (CN)
Openalex Percentile: Top 17%
Markov Chains and Monte Carlo Methods
DOIOpen Access PDFOpenAlex
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