Monotonic abstraction-refinement for CTL

Sharon Shoham, Orna Grumberg

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

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 hypertransitions. 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. We suggest an automatic construction of an initial abstract model and its successive refined models. We complete the framework by adjusting the BDD-based approach to the new monotonic framework. Thus, we obtain a monotonie, symbolic framework that is suitable for both verification and falsification of full CTL.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsKurt Jensen, Andreas Podelski
PublisherSpringer Verlag
Pages546-560
Number of pages15
ISBN (Print)354021299X, 9783540212997
DOIs
StatePublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2988
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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