Artifacts using LearnLib :: Test Support :: Learning Examples (24)

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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 (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://doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García, including merging heuristics such as the "evidence-driven state merging" (EDSM) and "minimum description length" (MDL) strategies. More details on these implementations can be ...

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.

Basic support for test driver creation

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.

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.

A simple, straightforward implementation of Dana Angluin's L* algorithm

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.

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.