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Computing Semicommutation Closures: a Machine Learning Approach

Maxime Bride 1 Pierre-Cyrille Héam 2, 1 Isabelle Jacques 2 
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 Nancy - Grand Est, LORIA - FM - Department of Formal Methods
Abstract : Semicommutation relations are simple rewriting relation on finite words using rules of the form ab → ba. In this paper we present how to use Angluin style machine learning algorithms to compute the image of regular language by the transitive closure of a semicommutation relation.
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Submitted on : Tuesday, December 2, 2014 - 10:37:17 AM
Last modification on : Wednesday, October 26, 2022 - 8:15:00 AM
Long-term archiving on: : Tuesday, March 3, 2015 - 10:05:31 AM


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  • HAL Id : hal-01087740, version 1


Maxime Bride, Pierre-Cyrille Héam, Isabelle Jacques. Computing Semicommutation Closures: a Machine Learning Approach. [Research Report] FEMTO-ST. 2014. ⟨hal-01087740⟩



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