Learning Formal Specifications from Membership and Preference Queries

Ameesh Shah, Marcell Vazquez-Chanlatte, Sebastian Junges, Sanjit A. Seshia

Keywords: formal methods, active learning, preference-based learning, specification mining

TL;DR: We extend active learning frameworks of formal specifications to support preference queries alongside membership queries, providing a more flexible and human-friendly approach to active specification learning.

Abstract: 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.

Submission Number: 27