Probabilistic Abstractions with Arbitrary Domains

  • Javier Esparza
  • Andreas Gaiser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6887)

Abstract

Recent work by Hermanns et al. and Kattenbelt et al. has extended counterexample-guided abstraction refinement (CEGAR) to probabilistic programs. These approaches are limited to predicate abstraction. We present a novel technique, based on the abstract reachability tree recently introduced by Gulavani et al., that can use arbitrary abstract domains and widening operators (in the sense of Abstract Interpretation). We show how suitable widening operators can deduce loop invariants difficult to find for predicate abstraction, and propose refinement techniques.

Keywords

Span Tree Markov Decision Process Stochastic Game Abstract Interpretation Concrete State 
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 2011

Authors and Affiliations

  • Javier Esparza
    • 1
  • Andreas Gaiser
    • 1
  1. 1.Fakultät für InformatikTechnische Universität MünchenGermany

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