Towards Efficient Active Learning of PDFA

Code

• github.com/neuralchecker/pyModelExtractor
  Officialnone★ 1
• github.com/neuralchecker/pymodelextractor_towards_efficient_active_learning_of_PDFA_experiments
  Officialnone★ 0

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.

Tasks

Reproductions