Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Learning Top-Down Tree Transformations with Regular Inspection

Adrien Boiret 1 Aurélien Lemay 1 Joachim Niehren 1
1 LINKS - Linking Dynamic Data
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 (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)
Complete list of metadata

https://hal.inria.fr/hal-01357631
Contributor : Inria Links <>
Submitted on : Tuesday, August 30, 2016 - 11:01:51 AM
Last modification on : Friday, December 11, 2020 - 6:44:06 PM

Identifiers

  • HAL Id : hal-01357631, version 1

Collections

Citation

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

Share

Metrics

Record views

257