libalf: the automata learning framework

  • Authors:
  • Benedikt Bollig;Joost-Pieter Katoen;Carsten Kern;Martin Leucker;Daniel Neider;David R. Piegdon

  • Affiliations:
  • LSV, ENS Cachan, CNRS;RWTH Aachen University;RWTH Aachen University;TU München;RWTH Aachen University;RWTH Aachen University

  • Venue:
  • CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
  • Year:
  • 2010

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Abstract

This paper presents libalf, a comprehensive, open-source library for learning formal languages libalf covers various well-known learning techniques for finite automata (e.g Angluin's L*, Biermann, RPNI etc.) as well as novel learning algorithms (such as for NFA and visibly one-counter automata) libalf is flexible and allows facilely interchanging learning algorithms and combining domain-specific features in a plug-and-play fashion Its modular design and C++ implementation make it a suitable platform for adding and engineering further learning algorithms for new target models (e.g., Büchi automata).