Abstract
Grammatical inference consists in learning formal grammars for unknown languages when given sequential learning data. Classically this data is raw: Strings that belong to the language and eventually strings that do not. In this paper, we present a generic setting allowing to express domain and typing background knowledge. Algorithmic solutions are provided to introduce this additional information efficiently in the classical state-merging automata learning framework. Improvement induced by the use of this background knowledge is shown on both artificial and real data.
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Coste, F., Fredouille, D., Kermorvant, C., de la Higuera, C. (2004). Introducing Domain and Typing Bias in Automata Inference. In: Paliouras, G., Sakakibara, Y. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2004. Lecture Notes in Computer Science(), vol 3264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30195-0_11
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DOI: https://doi.org/10.1007/978-3-540-30195-0_11
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