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Counterexamples for Expected Rewards

  • Tim Quatmann
  • Nils Jansen
  • Christian Dehnert
  • Ralf Wimmer
  • Erika Ábrahám
  • Joost-Pieter Katoen
  • Bernd Becker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9109)

Abstract

The computation of counterexamples for probabilistic systems has gained a lot of attention during the last few years. All of the proposed methods focus on the situation when the probabilities of certain events are too high. In this paper we investigate how counterexamples for properties concerning expected costs (or, equivalently, expected rewards) of events can be computed. We propose methods to extract a minimal subsystem which already leads to costs beyond the allowed bound. Besides these exact methods, we present heuristic approaches based on path search and on best-first search, which are applicable to very large systems when deriving a minimum subsystem becomes infeasible due to the system size. Experiments show that we can compute counterexamples for systems with millions of states.

Keywords

Model Check Target State Mixed Integer Linear Program Path Search Expected Reward 
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 2015

Authors and Affiliations

  • Tim Quatmann
    • 1
  • Nils Jansen
    • 1
  • Christian Dehnert
    • 1
  • Ralf Wimmer
    • 2
  • Erika Ábrahám
    • 1
  • Joost-Pieter Katoen
    • 1
  • Bernd Becker
    • 2
  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Albert-Ludwigs-UniversitätFreiburgGermany

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