Learning a Subclass of Linear Languages from Positive Structural Information

A method to infer a subclass of linear languages from positive structural information (i.e. skeletons) is presented. The characterization of the class and the analysis of the time and space complexity of the algorithm is exposed too. The new class, Terminal and Structural Distinguishable Linear Languages (TSDLL), is defined through an algebraic characterization and a pumping lemma. We prove that the proposed algorithm correctly identifies any TSDL language in the limit if structural information is presented. Furthermore, we give a definition of a characteristic structural set for any target grammar. Finally we present the conclusions of the work and some guidelines for future works.

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