Learning Formal Specifications from Membership and Preference Queries
Ameesh Shah Marcell Vazquez-Chanlatte Sebastian Junges Sanjit A. Seshia
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. Our
results suggest that learning from both modalities allows us to robustly and
conveniently identify specifications via membership and preferences.