%0 preprint %A Mayr, F. %E Yovine, S. %E Pan, F. %E Basset, N. %E Dang, T. %D 2022 %G eng %T Towards Efficient Active Learning of PDFA %U https://hdl.handle.net/20.500.12381/595 %X 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.