Model Checking Hierarchical Probabilistic Systems

  • Jun Sun
  • Songzheng Song
  • Yang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6447)


Probabilistic modeling is important for random distributed algorithms, bio-systems or decision processes. Probabilistic model checking is a systematic way of analyzing finite-state probabilistic models. Existing probabilistic model checkers have been designed for simple systems without hierarchy. In this paper, we extend the PAT toolkit to support probabilistic model checking of hierarchical complex systems. We propose to use PCSP#, a combination of Hoare’s CSP with data and probability, to model such systems. In addition to temporal logic, we allow complex safety properties to be specified by non-probabilistic PCSP# model. Validity of the properties (with probability) is established by refinement checking. Furthermore, we show that refinement checking can be applied to verify probabilistic systems against safety/co-safety temporal logic properties efficiently. We demonstrate the usability and scalability of the extended PAT checker via automated verification of benchmark systems and comparison with state-of-art probabilistic model checkers.


Model Check Temporal Logic Markov Decision Process Mutual Exclusion Probabilistic Choice 
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 2010

Authors and Affiliations

  • Jun Sun
    • 1
  • Songzheng Song
    • 3
  • Yang Liu
    • 2
  1. 1.Singapore University of Technology and DesignSingapore
  2. 2.National University of SingaporeSingapore
  3. 3.NUS Graduate School for Integrative Sciences and EngineeringSingapore

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