libalf
libalf: The Automata Learning Framework. 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).
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References in zbMATH (referenced in 6 articles )
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Sorted by year (- Björklund, Johanna; Fernau, Henning; Kasprzik, Anna: Polynomial inference of universal automata from membership and equivalence queries (2016)
- Garg, Pranav; Löding, Christof; Madhusudan, P.; Neider, Daniel: Quantified data automata for linear data structures: a register automaton model with applications to learning invariants of programs manipulating arrays and lists (2015)
- Bollig, Benedikt; Habermehl, Peter; Leucker, Martin; Monmege, Benjamin: A robust class of data languages and an application to learning (2014)
- Chen, Yu-Fang; Wang, Bow-Yaw: BULL: a library for learning algorithms of Boolean functions (2013) ioport
- Neider, Daniel: Reachability games on automatic graphs (2011)
- Bollig, Benedikt; Katoen, Joost-Pieter; Kern, Carsten; Leucker, Martin; Neider, Daniel; Piegdon, David R.: \urllibalf: The automata learning framework (2010) ioport