Artifacts using automata-core version 0.4.1

This artifact provides various common utility operations for analyzing and manipulating automata and graphs, such as traversal, minimization and copying.

Example learning setups to be used for integration testing

Infrastructure and core functionalities of LearnLib

A collection of basic algorithm for approximating equivalence queries

This artifact contains algorithms for incrementally constructing DFAs (prefix-closed and non-prefix-closed), Mealy machines, and Moore machines from a finite, incrementally growing set of example inputs/outputs.

A flexible, optimized version of Dana Angluin's L* algorithm

A learning algorithm, which distinguishes hypothesis states using a discrimination tree.

This artifact provides the implementation of the DHC learning algorithm as described in the paper "Automata Learning with on-the-Fly Direct Hypothesis Construction" (https://doi.org/10.1007/978-3-642-34781-8_19) by Maik Merten, Falk Howar, Bernhard Steffen, and Tiziana Margaria.

This artifact provides the implementation of the learning algorithm described in the book "An Introduction to Computational Learning Theory" by Michael Kearns and Umesh Vazirani.

This artifact contains adapter classes for treating the Automaton objects from the BRICS library (http://www.brics.dk/automaton) as AutomataLib models.