Data Structures with Arithmetic Constraints: a Non-disjoint Combination

Enrica Nicolini 1 Christophe Ringeissen 1 Michael Rusinowitch 1
1 CASSIS - Combination of approaches to the security of infinite states systems
FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174), INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We apply an extension of the Nelson-Oppen combination method to develop a decision procedure for the non-disjoint union of theories modeling data structures with a counting operator and fragments of arithmetic. We present some data structures and some fragments of arithmetic for which the combination method is complete and effective. To achieve effectiveness, the combination method relies on particular procedures to compute sets that are representative of all the consequences over the shared theory. We show how to compute these sets by using a superposition calculus for the theories of the considered data structures and various solving and reduction techniques for the fragments of arithmetic we are interested in, including Gauss elimination, Fourier-Motzkin elimination and Groebner bases computation.
Type de document :
Communication dans un congrès
Silvio Ghilardi and Roberto Sebastiani. 7th International Symposium on Frontiers of Combining Systems - FroCoS'2009, Sep 2009, Trento, Italy. Springer, 5749, pp.335-350, 2009, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-642-04222-5_20〉
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https://hal.inria.fr/inria-00428421
Contributeur : Christophe Ringeissen <>
Soumis le : mercredi 28 octobre 2009 - 17:29:16
Dernière modification le : vendredi 6 juillet 2018 - 15:06:10

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Enrica Nicolini, Christophe Ringeissen, Michael Rusinowitch. Data Structures with Arithmetic Constraints: a Non-disjoint Combination. Silvio Ghilardi and Roberto Sebastiani. 7th International Symposium on Frontiers of Combining Systems - FroCoS'2009, Sep 2009, Trento, Italy. Springer, 5749, pp.335-350, 2009, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-642-04222-5_20〉. 〈inria-00428421〉

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