LearnLib 19.0.0 API
Modules
Module
Description
This module provides the implementation of the AAAR learning algorithm as described in the paper
Automata Learning with Automated Alphabet Abstraction
Refinement by Falk Howar, Bernhard Steffen, and Maik Merten.
This module provides the implementation of the ADT learning algorithm as described in the Master thesis Active Automata Learning with Adaptive Distinguishing Sequences by Markus
Frohme.
This module provides the implementation of the DHC learning algorithm as described in the paper
Automata Learning with on-the-Fly Direct Hypothesis
Construction by Maik Merten, Falk Howar, Bernhard Steffen, and Tiziana Margaria.
This module provides the implementation of the learning algorithm described in the book "An
Introduction to Computational Learning Theory" by Michael Kearns and Umesh Vazirani.
This module 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 by Falk Howar and Bernhard Steffen.
This module provides the implementation of the L# algorithm as described in the paper A New Approach for Active Automata Learning Based on
Apartness by Frits Vaandrager, Bharat Garhewal, Jurriaan Rot, and Thorsten Wißmann.
This module provides the implementation of the L* learning algorithm described in the paper
Learning Regular Sets from Queries and Counterexamples by
Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets by Oded Maler and
Amir Pnueli or Inference of finite automata using homing
sequences) by Ronald L. Rivest and Robert E. Schapire.
This module provides the implementation of the NL* learning algorithm as described in the paper
Angluin-Style Learning of NFA by Benedikt Bollig, Peter
Habermehl, Carsten Kern, and Martin Leucker.
This module provides the implementation of the Observation-Pack learning algorithm as described in the PhD thesis Active learning of interface programs by Falk Howar.
This module 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 by Malte Isberner.
This module provides the implementation of the "onward subsequential transducer inference algorithm" (OSTIA) learning
algorithm as presented in the paper Learning Subsequential Transducers
for Pattern Recognition Interpretation Tasks by Jose Oncina, Pedro García, and Enrique Vidal.
This module 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 and From Languages
to Behaviors and Back by Markus Frohme and Bernhard Steffen.
This module 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 by Jose Oncina and Pedro García, including merging heuristics such as
the "evidence-driven state merging" (EDSM) and "minimum description length" (MDL) strategies.
This module provides the implementation of the Sparse OT learning algorithm as described in the paper
Learning Mealy Machines with Sparse Observation Tables
by Wolffhardt Schwabe, Paul Kogel, and Sabine Glesner.
This module provides the implementation of the TTT algorithm as described in the paper The TTT Algorithm: A Redundancy-Free Approach to Active Automata
Learning by Malte Isberner, Falk Howar, and Bernhard Steffen.
This module 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 by Malte Isberner.
This module provides the core interfaces of LearnLib.
This module provides a collection of algorithms for handling counterexamples in automata learning.
This module provides a collection of utility methods for learning setups (oracle wrappers, etc.).
This module provides data structures shared by multiple learning algorithms of LearnLib.
This module provides basic support for test driver creation.
This module provides utilities for simulating
SULs.This module provides caches to avoid posing duplicate membership queries.
This module provides a reuse tree for (intelligently) caching membership queries as described in the paper Reusing System States by Active Learning Algorithms by Oliver
Bauer, Johannes Neubauer, Bernhard Steffen, and Falk Howar.
This module provides filters for collecting statistical data.
This module provides a collection of symbol filters.
This module provides translation utilities for mapping abstract hypothesis symbols to concrete
SUL symbols.This module provides a collection of emptiness oracles.
This module provides a collection of equivalence oracles.
This module provides a collection of membership oracles.
This module provides support for posing membership queries in parallel.
This module provides a collection of property oracles.
This module provides a collection of utility methods to parse LearnLib specific settings.
This module provides support classes for easily writing unit tests for various components of LearnLib.
This module provides example learning setups, to be used for integration testing.
This module provides support classes for easily writing integration test cases for learning algorithms.