2014
DOI: 10.1145/2627352
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Incremental Bisimulation Abstraction Refinement

Abstract: Abstraction refinement techniques in probabilistic model checking are prominent approaches for verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a counterexample. This article proposes an abstraction refinement approach for the probabilistic computation tree logic (PCTL), which is based on incrementally computing a sequence of may-and must-quotient automata. These are induced by depth-bounded bisimulation … Show more

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Cited by 3 publications
(3 citation statements)
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References 39 publications
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“…Several different abstraction approaches have been proposed for probabilistic systems [12,21,27,17,9,25,29]. Our work builds on the game-based abstraction refinement algorithm presented in [25,18].…”
Section: Related Workmentioning
confidence: 99%
“…Several different abstraction approaches have been proposed for probabilistic systems [12,21,27,17,9,25,29]. Our work builds on the game-based abstraction refinement algorithm presented in [25,18].…”
Section: Related Workmentioning
confidence: 99%
“…An approach was proposed [384] to accelerate the model checking of PCTL formulas on probabilistic automata, by iteratively refining an abstraction of a probabilistic automaton, using incrementally computed bisimulations and without resorting to any kind of counterexample analysis.…”
Section: Bisimulation Tools For Probabilistic and Stochastic Systemsmentioning
confidence: 99%
“…In the same vein, Dehnert et al proposed symbolic probabilistic bisimulation minimisation to reduce computation time for model checking PCTL in a setting for DTMCs [13], where an SMT solver is exploited to do the splitting of blocks. Partition reduction modulo probabilistic bisimulation is also used as an ingredient in a counter-example guided abstraction refinement approach (CEGAR) for model checking for PCTL by Lei Song et al in [14].…”
Section: Related Workmentioning
confidence: 99%