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Reduction and Refinement Strategies for Probabilistic Analysis

  • Pedro R. D’Argenio
  • Bertrand Jeannet
  • Henrik E. Jensen
  • Kim G. Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2399)

Abstract

We report on new strategies for model checking quantitative reachability properties of Markov decision processes by successive refinements. In our approach, properties are analyzed on abstractions rather than directly on the given model. Such abstractions are expected to be significantly smaller than the original model, and may safely refute or accept the required property. Otherwise, the abstraction is refined and the process repeated. As the numerical analysis involved in settling the validity of the property is more costly than the refinement process, the method profits from applying such numerical analysis on smaller state spaces. The method is significantly enhanced by a number of novel strategies: a strategy for reducing the size of the numerical problems to be analyzed by identification of so-called essential states, and heuristic strategies for guiding the refinement process.

Keywords

Linear Programming Problem Essential State Simple Path Reachable State Initial Partition 
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 2002

Authors and Affiliations

  • Pedro R. D’Argenio
    • 1
  • Bertrand Jeannet
    • 2
  • Henrik E. Jensen
    • 3
  • Kim G. Larsen
    • 3
  1. 1.FaMAFUniversidad Nacional de CórdobaCórdobaArgentina
  2. 2.IRISA - INRIARennes CedexFrance
  3. 3.BRICSAalborg UniversityAalborgDenmark

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