Active Learning of Sequential Transducers with Side Information about the Domain
This work addresses efficiency in automata learning for formal verification or language processing, but it is incremental as it builds on graybox learning with specific side information.
The paper tackled the problem of active learning for subsequential string transducers by leveraging known regular overapproximations of the domain, resulting in an algorithm that reduces the number of costly equivalence queries compared to classical methods.
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 using string equation solvers that uses this knowledge to learn subsequential string transducers with a better guarantee on the required number of equivalence queries than classical active learning.