I am very happy to announce that we finally have a nice introduction to our dfasat tool: a short python notebook tutorial  (html preview) originally developed for a 2-hour hands-on session at the 3TU BSR winter school. The notebook works you through basic usage and parameter setting. It also contains a small task to familiarize the user with the effect of different parameter settings. At the moment, dfasat has about 30 different options to choose from. Some can be combined, whereas other combinations have never been tried in combination….Continue Reading “A passive automata learning tutorial with dfasat”

Table of the performance metrics.

The Sequence PredIction ChallengE (SPICE), organized and co-located with the International Conference on Grammatical Inference (ICGI) 2016,  was won by Chihiro Shibata, who combined LSTM neural networks and strictly piecewise grammars (SP-k, proposed by Heinz et al), the latter capturing long-term dependencies in the input words. The combination beat the competitors using “pure” LSTM- and CNN-based neural networks. Overall, all networks used were not very deep (2 hidden layers), and deeper networks decreased performance. The task of the competition was to predict a (ranked) list of most…Continue Reading “The Performance of PDFA-Learning in the SPiCE Competition”

This year a team from the Radboud University and TU Delft used automata learning to compete in the RERS challenge 2016. The challenge provides (generated) source code where the challenge is to (dis)prove certain LTL formulas and to analyze which error states are reachable. Information on this challenge can be found here: http://www.rers-challenge.org/2016/. Commonly, learning is not used in this competition and only white-box methods are used. This year, however, automata learning was applied to great success. For the problems where LTL formulas had to be…Continue Reading “Succes at the RERS challenge 2016 for automata learning”