Artifacts using LearnLib :: Test Support :: Learner IT Support (21)

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://dx.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 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.

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

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://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García. More details on the blue-fringe version of this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

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

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" (https://doi.org/10.1007/978-3-031-15629-8_17) by Falk Howar and Bernhard Steffen.

A learning algorithm, which distinguishes hypothesis states using a discrimination tree (visibly push-down automata variant).