Quantitative Analysis under Fairness Constraints

  • Christel Baier
  • Marcus Groesser
  • Frank Ciesinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5799)


It is well-known that fairness assumptions can be crucial for verifying progress, reactivity or other liveness properties for interleaving models. This also applies to Markov decision processes as an operational model for concurrent probabilistic systems and the task to establish tight lower or upper probability bounds for events that are specified by liveness properties. In this paper, we study general notions of strong and weak fairness constraints for Markov decision processes, formalized in an action- or state-based setting. We present a polynomially time-bounded algorithm for the quantitative analysis of an MDP against ω-automata specifications under fair worst- or best-case scenarios. Furthermore, we discuss the treatment of strong and weak fairness and process fairness constraints in the context of partial order reduction techniques for Markov decision processes that have been realized in the model checker LiQuor and rely on a variant of Peled’s ample set method.


Model Check Markov Decision Process Atomic Proposition Liveness Property Fairness Condition 
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 2009

Authors and Affiliations

  • Christel Baier
    • 1
  • Marcus Groesser
    • 1
  • Frank Ciesinski
    • 1
  1. 1.Technische Universtät DresdenDresdenGermany

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