Artifacts using automata-core version 0.6.0

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

Generic automata serialization core

A collection of basic algorithm for approximating equivalence queries

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

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.

This artifact provides the implementation of the TTT algorithm as described in the paper "The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning" (https://doi.org/10.1007/978-3-319-11164-3_26) by Malte Isberner, Falk Howar, and Bernhard Steffen.

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 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.