Learning Formal Specifications from Membership and Preference Queries

Understand

Active learning is a well-studied approach to learning formal specifications, such as automata.

• In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels.
• The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only.
• We instantiate our framework in two different domains, demonstrating the generality of our approach.