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Deciding Bisimilarities on Distributions

  • Christian Eisentraut
  • Holger Hermanns
  • Julia Krämer
  • Andrea Turrini
  • Lijun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8054)

Abstract

Probabilistic automata (PA) are a prominent compositional concurrency model. As a way to justify property-preserving abstractions, in the last years, bisimulation relations over probability distributions have been proposed both in the strong and the weak setting. Different to the usual bisimulation relations, which are defined over states, an algorithmic treatment of these relations is inherently hard, as their carrier set is uncountable, even for finite PAs. The coarsest of these relation, weak distribution bisimulation, stands out from the others in that no equivalent state-based characterisation is known so far. This paper presents an equivalent state-based reformulation for weak distribution bisimulation, rendering it amenable for algorithmic treatment. Then, decision procedures for the probability distribution-based bisimulation relations are presented.

Keywords

Decision Algorithm Weak Transition Probabilistic Automaton Preserve Transition Weak Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Eisentraut
    • 1
  • Holger Hermanns
    • 1
  • Julia Krämer
    • 1
  • Andrea Turrini
    • 1
  • Lijun Zhang
    • 2
    • 3
    • 1
  1. 1.Computer ScienceSaarland UniversitySaarbrückenGermany
  2. 2.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingChina
  3. 3.DTU InformaticsTechnical University of DenmarkDenmark

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