Towards Efficient Active Learning of PDFA

Franz Mayr, Sergio Yovine, Federico Pan, Nicolas Basset, Thao Dang

Abstract: We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.