Artifacts using automata-commons-util version 0.12.0

Deserializers for the Finite State Machine format

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://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.

(De-)Serializers for the legacy LearnLibV2 format

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 model checker presented in the paper "M3C: Modal Meta Model Checking" (https://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://doi.org/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/), ...

This artifact provides the implementation of the VPA adaption of the TTT learning algorithm as presented 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 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 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.

(De-)Serializers for the Simple Automaton Format

This artifact provides the implementation of the ADT learning algorithm as described in the Master thesis "Active Automata Learning with Adaptive Distinguishing Sequences" (http://arxiv.org/abs/1902.01139) by Markus Frohme.