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Learning Top-Down Tree Transducers with Regular Domain 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 - 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, 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.
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Submitted on : Monday, August 29, 2016 - 2:00:38 PM
Last modification on : Friday, December 11, 2020 - 6:44:06 PM
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Adrien Boiret, Aurélien Lemay, Joachim Niehren. Learning Top-Down Tree Transducers with Regular Domain Inspection. International Conference on Grammatical Inference 2016, Oct 2016, Delft, Netherlands. ⟨hal-01357186⟩

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