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.
KeywordsAutomata Inference Background Knowledge
Unable to display preview. Download preview PDF.