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Preprints, Working Papers, ... Year : 2016

Learning Top-Down Tree Transformations with Regular Inspection

Adrien Boiret
  • Function : Author
  • PersonId : 958173
Aurélien Lemay
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  • PersonId : 958887
Joachim Niehren

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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Dates and versions

hal-01357631 , version 1 (30-08-2016)

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  • HAL Id : hal-01357631 , version 1

Cite

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