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Compositional Verification and Optimization of Interactive Markov Chains

  • Holger Hermanns
  • Jan Krčál
  • Jan Křetínský
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8052)

Abstract

Interactive Markov chains (IMC) are compositional behavioural models extending labelled transition systems and continuous-time Markov chains. We provide a framework and algorithms for compositional verification and optimization of IMC with respect to time-bounded properties. Firstly, we give a specification formalism for IMC. Secondly, given a time-bounded property, an IMC component and the assumption that its unknown environment satisfies a given specification, we synthesize a scheduler for the component optimizing the probability that the property is satisfied in any such environment.

Keywords

External Action Internal Transition Stochastic Game Parallel Composition Label Transition System 
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

  • Holger Hermanns
    • 1
  • Jan Krčál
    • 2
  • Jan Křetínský
    • 2
    • 3
  1. 1.Computer ScienceSaarland UniversitySaarbrückenGermany
  2. 2.Faculty of InformaticsMasaryk UniversityCzech Republic
  3. 3.Institut für InformatikTechnical University MunichGermany

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