LearnLib 0.18.0 API

Modules

Module	Description
de.learnlib.algorithm.aaar	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.
de.learnlib.algorithm.adt	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.
de.learnlib.algorithm.dhc	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.
de.learnlib.algorithm.kv	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.
de.learnlib.algorithm.lambda	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.
de.learnlib.algorithm.lsharp	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.
de.learnlib.algorithm.lstar	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.
de.learnlib.algorithm.nlstar	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.
de.learnlib.algorithm.observationpack	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.
de.learnlib.algorithm.observationpack.vpa	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.
de.learnlib.algorithm.ostia	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.
de.learnlib.algorithm.procedural	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.
de.learnlib.algorithm.rpni	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.
de.learnlib.algorithm.ttt	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.
de.learnlib.algorithm.ttt.vpa	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.
de.learnlib.api	This module provides the core interfaces of LearnLib.
de.learnlib.common.counterexample	This module provides a collection of algorithms for handling counterexamples in automata learning.
de.learnlib.common.util	This module provides a collection of utility methods for learning setups (oracle wrappers, etc.).
de.learnlib.datastructure	This module provides data structures shared by multiple learning algorithms of LearnLib.
de.learnlib.driver	This module provides basic support for test driver creation.
de.learnlib.driver.simulator	This module provides utilities for simulating SULs.
de.learnlib.filter.cache	This module provides caches to avoid posing duplicate membership queries.
de.learnlib.filter.reuse	This module provides a reuse tree for (intelligently) caching membership queries.
de.learnlib.filter.statistic	This module provides filters for collecting statistical data.
de.learnlib.mapper	This module provides translation utilities for mapping abstract hypothesis symbols to concrete SUL symbols.
de.learnlib.oracle.emptiness	This module provides a collection of emptiness oracles.
de.learnlib.oracle.equivalence	This module provides a collection of equivalence oracles.
de.learnlib.oracle.membership	This module provides a collection of membership oracles.
de.learnlib.oracle.parallelism	This module provides support for posing membership queries in parallel.
de.learnlib.oracle.property	This module provides a collection of property oracles.
de.learnlib.setting	This module provides a collection of utility methods to parse LearnLib specific settings.
de.learnlib.testsupport	This module provides support classes for easily writing unit tests for various components of LearnLib.
de.learnlib.testsupport.example	This module provides example learning setups, to be used for integration testing.
de.learnlib.testsupport.it	This module provides support classes for easily writing integration test cases for learning algorithms.