Artifacts using automata-commons-util version 0.12.0

This artifact provides the implementations of various learning algorithms based on the "lazy partition refinement" concept as described in the paper "Active Automata Learning as Black-Box Search and Lazy Partition Refinement" (https://doi.org/10.1007/978-3-031-15629-8_17) by 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 ...

Basic support for test driver creation

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 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 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 Observation-Pack learning algorithm as described in the PhD thesis "Active learning of interface programs" (http://doi.org/10.17877/DE290R-4817) by Falk Howar.

(De-)Serializers for the Textual Automaton Format

Serializers for LTSmin's Enumerated Table Format