Active Automata Learning with Advice

TL;DR

This paper introduces an enhanced automata learning framework that incorporates advice through string rewriting systems to reduce query complexity, demonstrating significant empirical improvements.

Contribution

It extends active automata learning by integrating deductive advice, enabling more efficient query answering and reducing teacher burden.

Findings

Substantial reduction in query complexity observed.

Effective adaptation of Angluin-style algorithms to the advice framework.

Empirical results show improved learning efficiency.

Abstract

We present an extended automata learning framework that combines active automata learning with deductive inference. The learning algorithm asks membership and equivalence queries as in the original framework, but it is also given advice, which is used to infer answers to queries when possible and reduce the burden on the teacher. We consider advice given via string rewriting systems, which specify equivalence of words w.r.t. the target languages. The main motivation for the proposed framework is to reduce the number of queries. We show how to adapt Angluin-style learning algorithms to this framework with low overhead. Finally, we present empirical evaluation of our approach and observe substantial improvement in query complexity.