Support classes for easily writing integration test cases for learning algorithms

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

This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations ...

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 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, ...

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" ...

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 ...

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.

This artifact provides the implementations of various learning algorithms based on the "optimal MAT learning" concept as described in the paper "Active Automata Learning as Black-Box Search and Lazy Partition Refinement" ...

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

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" ...