Artifacts using automata-api version 0.7.0

Basic support for test driver creation

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.

An artifact that aggregates all other artifacts of AutomataLib to produce an Uber-JAR that can be used in non-maven environments. Likewise, this single artifact may be used in maven-aware environments to declare a dependency on all AutomataLib artifacts.

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 (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García, including merging heuristics such as the "evidence-driven state merging" (EDSM) and "minimum description length" (MDL) strategies. More details on these implementations can be ...

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

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

Data- and utility classes for Observation Tables

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