  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.
