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.
https://hal.inria.fr/hal-01357186 Contributor : Inria LinksConnect in order to contact the contributor Submitted on : Monday, August 29, 2016 - 2:00:38 PM Last modification on : Wednesday, March 23, 2022 - 3:51:22 PM Long-term archiving on: : Wednesday, November 30, 2016 - 1:58:13 PM
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⟩