Artifacts using automata-util version 0.5.2

Example learning setups to be used for integration testing

Infrastructure and core functionalities of LearnLib

Generic automata serialization core

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.

Common interfaces providing features supported by various learning algorithms

This artifact provides a visualization implementation based on the JUNG (https://jung.sourceforge.net/) library.

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.