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

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 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.

This artifact provides the implementations of various learning algorithms for systems of procedural automata such as the ones described in the papers "Compositional learning of mutually recursive procedural systems (https://doi.org/10.1007/s10009-021-00634-y) and "From Languages to Behaviors and Back" (https://doi.org/10.1007/978-3-031-15629-8_11) by Markus Frohme and Bernhard Steffen.

This artifact provides the integration of the model checker "LTSmin" (https://ltsmin.utwente.nl/) as described in the paper "Sound Black-Box Checking in the LearnLib" (https://dx.doi.org/10.1007/s11334-019-00342-6) by Jeroen Meijer and Jaco van de Pol. Note that this implementation requires a local installation of the LTSmin binaries (see https://ltsmin.utwente.nl/) which are not explicitly included in this artifact due to packaging reasons.

This package contains Java programs for reproducing the experiments, and analysis of experimental data, from the following article: Cicirello, V.A. On Fitness Landscape Analysis of Permutation Problems: From Distance Metrics to Mutation Operator Selection. Mobile Networks and Applications (2022). https://doi.org/10.1007/s11036-022-02060-z.

This artifact provides the implementation of the model checker presented in the paper "M3C: Modal Meta Model Checking" (https://dx.doi.org/10.1007/978-3-030-00244-2_15) by Bernhard Steffen and Alnis Murtovi. The paper is based on "Model Checking for Context-Free Processes" (https://link.springer.com/chapter/10.1007/BFb0084787) by Olaf Burkart and Bernhard Steffen. Note that this implementation requires a runtime dependency to a specific ADDLib backend (see https://add-lib.scce.info/), which is not explicitly included in this artifact due to packaging reasons.