Learning Formal Specifications from Membership and Preference Queries
This work addresses a bottleneck in active specification learning for researchers in formal methods, though it appears incremental as it builds on existing approaches by adding preferences.
The paper tackles the problem of learning formal specifications by extending active learning to incorporate both membership labels and pair-wise preferences, showing that this combination enables robust and convenient identification of specifications.
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.