Introducing Domain and Typing Bias in Automata Inference

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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