Monotonic Abstraction-Refinement for CTL

The goal of this work is to improve the efficiency and effectiveness of the abstraction-refinement framework for CTL over the 3-valued semantics. We start by proposing a symbolic (BDD-based) approach for this framework. Next, we generalize the definition of abstract models in order to provide a monotonic abstraction-refinement framework. To do so, we introduce the notion of hyper-transitions. For a given set of abstract states, this results in a more precise abstract model in which more CTL formulae can be proved or disproved.

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