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Probabilistic Model Checking of Labelled Markov Processes via Finite Approximate Bisimulations

  • Alessandro Abate
  • Marta Kwiatkowska
  • Gethin Norman
  • David Parker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8464)

Abstract

This paper concerns labelled Markov processes (LMPs), probabilistic models over uncountable state spaces originally introduced by Prakash Panangaden and colleagues. Motivated by the practical application of the LMP framework, we study its formal semantics and the relationship to similar models formulated in control theory. We consider notions of (exact and approximate) probabilistic bisimulation over LMPs and, drawing on methods from both formal verification and control theory, propose a simple technique to compute an approximate probabilistic bisimulation of a given LMP, where the resulting abstraction is characterised as a finite-state labelled Markov chain (LMC). This construction enables the application of automated quantitative verification and policy synthesis techniques over the obtained abstract model, which can be used to perform approximate analysis of the concrete LMP. We illustrate this process through a case study of a multi-room heating system that employs the probabilistic model checker PRISM.

Keywords

Model Check Markov Decision Process Atomic Proposition Probabilistic Model Check Default Action 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Alessandro Abate
    • 1
  • Marta Kwiatkowska
    • 1
  • Gethin Norman
    • 2
  • David Parker
    • 3
  1. 1.Department of Computer ScienceUniversity of OxfordUK
  2. 2.School of Computing ScienceUniversity of GlasgowUK
  3. 3.School of Computer ScienceUniversity of BirminghamUK

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