Active Learning of Sequential Transducers with Side Information about the Domain

TL;DR

This paper introduces an active learning algorithm for subsequential string transducers that leverages domain knowledge to reduce the number of costly equivalence queries needed for learning.

Contribution

It presents a novel algorithm utilizing string equation solvers that improves query efficiency by incorporating known domain overapproximations.

Findings

The algorithm requires fewer equivalence queries than classical methods.

Incorporating domain knowledge accelerates the learning process.

The approach outperforms traditional active learning in query complexity.

Abstract

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.