Learning Top-Down Tree Transformations with Regular Inspection

Adrien Boiret 1 Aurélien Lemay 1 Joachim Niehren 1
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Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : We study the problem of how to learn tree transformations on a given regular tree domain from a finite sample of input-output examples. We assume that the target tree transformation can be defined by a deterministic top-down tree transducer with regular domain inspection (DTOPi:reg). An RPNI style learning algorithm that solves this problem in polynomial time and with polynomially many examples was presented at Pods'2010 (https://hal.inria.fr/inria-00460489v2), but restricted to the case of path-closed regular domains. In this paper, we show that this restriction can be removed. For this, we present a new normal form for DTOPi:reg by extending the Myhill-Nerode theorem for DTOP to regular domain inspections in a nontrivial manner. The RPNI style learning algorithm can also be lifted but becomes more involved too. This is an extended version of a paper published in ICGI 2016 (https://hal.inria.fr/hal-01357186)
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Pré-publication, Document de travail
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Soumis le : mardi 30 août 2016 - 11:01:51
Dernière modification le : mardi 3 juillet 2018 - 11:29:49


  • HAL Id : hal-01357631, version 1



Adrien Boiret, Aurélien Lemay, Joachim Niehren. Learning Top-Down Tree Transformations with Regular Inspection. 2016. 〈hal-01357631〉



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