Learning Top-Down Tree Transformations with Regular Inspection
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)