Download de.learnlib JAR files with all dependencies
learnlib-ostia from group de.learnlib (version 0.17.0)
This artifact 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" (https://doi.org/10.1109/34.211465) by Jose Oncina, Pedro García, and Enrique Vidal.
learnlib-algorithms-passive-parent from group de.learnlib (version 0.17.0)
Parent module for (passive) automata learning algorithms shipped with LearnLib
Group: de.learnlib Artifact: learnlib-algorithms-passive-parent
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learnlib-ttt-vpa from group de.learnlib (version 0.17.0)
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://dx.doi.org/10.17877/DE290R-16359) by Malte Isberner.
learnlib-procedural from group de.learnlib (version 0.17.0)
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.
learnlib-observation-pack-vpa from group de.learnlib (version 0.17.0)
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://dx.doi.org/10.17877/DE290R-16359) by Malte Isberner.
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learnlib-nlstar from group de.learnlib (version 0.17.0)
This artifact provides the implementation of the NL* learning algorithm as described in the paper "Angluin-Style
Learning of NFA" (http://ijcai.org/Proceedings/09/Papers/170.pdf) by Benedikt Bollig, Peter Habermehl,
Carsten Kern, and Martin Leucker.
learnlib-dhc from group de.learnlib (version 0.17.0)
The Direct Hypothesis Construction algorithm for active learning of Mealy machines
learnlib-adt from group de.learnlib (version 0.17.0)
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.
learnlib-aaar from group de.learnlib (version 0.17.0)
This artifact provides the implementation of the AAAR learning algorithm as described in the paper "Automata
Learning with Automated Alphabet Abstraction Refinement" (https://dx.doi.org/10.1007/978-3-642-18275-4_19) by
Falk Howar, Bernhard Steffen, and Maik Merten.
learnlib-ttt from group de.learnlib (version 0.17.0)
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.
learnlib-oml from group de.learnlib (version 0.17.0)
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.
learnlib-observation-pack from group de.learnlib (version 0.17.0)
This artifact provides the implementation of the Observation-Pack learning algorithm as described in the PhD
thesis "Active learning of interface programs" (http://dx.doi.org/10.17877/DE290R-4817) by Falk Howar.
learnlib-lstar from group de.learnlib (version 0.17.0)
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 and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular
Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata
using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.
learnlib-datastructure-ot from group de.learnlib (version 0.17.0)
Data- and utility classes for Observation Tables
learnlib-kearns-vazirani from group de.learnlib (version 0.17.0)
The automata learning algorithm described by Kearns & Vazirani
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