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).
This work is partially supported by the DAAD (Procope 2009).
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Bollig, B., Katoen, JP., Kern, C., Leucker, M., Neider, D., Piegdon, D.R. (2010). libalf: The Automata Learning Framework. In: Touili, T., Cook, B., Jackson, P. (eds) Computer Aided Verification. CAV 2010. Lecture Notes in Computer Science, vol 6174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14295-6_32
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DOI: https://doi.org/10.1007/978-3-642-14295-6_32
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