Group: LearnLib

Sort by:Popular▼

✓Popular✓Newest

Infrastructure and core interfaces of LearnLib

A collection of membership oracles

A collection of utility methods for learning setups (oracle wrappers, etc.)

A collection of algorithms for handling counterexamples in automata learning

A collection of equivalence oracles

Utilities for simulating SULs

Caches to avoid posing duplicate membership queries

Module for data structures shared by multiple learning algorithms of LearnLib

Filters for collecting statistical data

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 ...

Support for posing membership queries in parallel

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 ...

Basic support for test driver creation

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 implementations of various learning algorithms based on the "lazy partition refinement" concept as described in the paper "Active Automata Learning as Black-Box Search and Lazy Partition Refinement" ...

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.

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" ...

A collection of utility methods to parse LearnLib specific settings.

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, ...

Data- and utility classes for Discrimination Trees