Active Learning of Sequential Transducers with Side Information About the Domain

• Published in: Developments in Language Theory pp. 54 - 65
• Main Authors: Berthon, Raphaël, Boiret, Adrien, Pérez, Guillermo A., Raskin, Jean-François
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3030815072; 9783030815073
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-81508-0_5

Active learning is a setting in which a student queries a teacher, through membership and equivalence queries, in order to learn a language. Performance on these algorithms is often measured in the number of queries required to learn a target, with an emphasis on costly equivalence queries. In graybox learning, the learning process is accelerated by foreknowledge of some information on the target. Here, we consider graybox active learning of subsequential string transducers, where a regular overapproximation of the domain is known by the student. We show that there exists an algorithm to learn subsequential string transducers with a better guarantee on the required number of equivalence queries than classical active learning.