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Long-Run Rewards for Markov Automata

  • Yuliya Butkova
  • Ralf Wimmer
  • Holger Hermanns
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10206)

Abstract

Markov automata are a powerful formalism for modelling systems which exhibit nondeterminism, probabilistic choices and continuous stochastic timing. We consider the computation of long-run average rewards, the most classical problem in continuous-time Markov model analysis. We propose an algorithm based on value iteration. It improves the state of the art by orders of magnitude. The contribution is rooted in a fresh look on Markov automata, namely by treating them as an efficient encoding of CTMDPs with – in the worst case – exponentially more transitions.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.Albert-Ludwigs-Universität FreiburgFreiburg im BreisgauGermany

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