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International Workshop on Verification, Model Checking, and Abstract Interpretation

VMCAI 2012: Verification, Model Checking, and Abstract Interpretation pp 396–411Cite as

A General Framework for Probabilistic Characterizing Formulae

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7148))

Abstract

Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward simulations. This paper shows how their constructions and proofs can follow from a single common technique.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, California State University Long Beach, USA

    Joshua Sack

  2. DTU Informatics, Technical University of Denmark, Denmark

    Lijun Zhang

Authors
  1. Joshua Sack
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  2. Lijun Zhang
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Editor information

Editors and Affiliations

  1. Swiss Federal Institute of Technology Lausanne (EPFL), IC IIF LARA INR 318, Station 14, 1015, Lausanne, Switzerland

    Viktor Kuncak

  2. Institut für Informatik, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany

    Andrey Rybalchenko

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Sack, J., Zhang, L. (2012). A General Framework for Probabilistic Characterizing Formulae. In: Kuncak, V., Rybalchenko, A. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2012. Lecture Notes in Computer Science, vol 7148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27940-9_26

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  • DOI: https://doi.org/10.1007/978-3-642-27940-9_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27939-3

  • Online ISBN: 978-3-642-27940-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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