LearnLib: a framework for extrapolating behavioral models

Harald Raffelt (1) , Bernhard Steffen (2) , Therese Berg (3) , Tiziana Margaria (4)

1 TU - Technische Universität Dortmund [Dortmund]
2 Computer Science Department [Dortmund]
3 DIT-UPPSALA - Department of Information Technology
4 University of Potsdam = Universität Potsdam

In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure their own tailored learning scenarios, which exploit specific properties of their envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios.

Mots clés: Automata learning; Domain-specific optimization; Experimentation; Software library; Grammar inference

Domaines: Apprentissage [cs.LG]

Harald Raffelt, Bernhard Steffen, Therese Berg, Tiziana Margaria. LearnLib: a framework for extrapolating behavioral models. International Journal on Software Tools for Technology Transfer, 2009, 11 (5), pp.393-407. ⟨10.1007/s10009-009-0111-8⟩. ⟨inria-00459959⟩