Artifacts using LearnLib :: Commons :: Counterexamples (15)

Sort by:Popular▼

✓Popular✓Newest

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 and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" ...

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 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 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.

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

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.

An artifact that aggregates all other artifacts of LearnLib 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 LearnLib artifacts.

This artifact provides the implementation of the VPA adaption of the Observation-Pack learning algorithm as discussed in the PhD thesis "Foundations of Active Automata Learning: An Algorithmic Perspective" (https://doi.org/10.17877/DE290R-16359) by Malte Isberner.

This artifact provides the implementation of the AAAR learning algorithm as described in the paper "Automata Learning with Automated Alphabet Abstraction Refinement" (https://doi.org/10.1007/978-3-642-18275-4_19) by Falk Howar, Bernhard Steffen, and Maik Merten.