Learning deterministically recognizable tree series - Revisited

Abstract

We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The learning is exact, supervised, and uses an adapted minimal adequate teacher; a learning model introduced by Angluin. Our algorithm learns a minimal deterministic wta that recognizes the taught tree series and runs in polynomial time in the size of that wta and the size of the provided counterexamples. Compared to the original algorithm, we show how to handle non-final states in the learning process; this problem was posed as an open problem in [Drewes, Vogler: Learning Deterministically Recognizable Tree Series, J. Autom. Lang. Combin. 2007]. © Springer-Verlag Berlin Heidelberg 2007.