2023/07/19 by Ameesh Shah, Marcell Vazquez-Chanlatte, Shah, Ameesh +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Chemical Synthesis and Analysis #FOS: Computer and information sciences #Formal Languages and Automata Theory (cs.FL) #Machine Learning (cs.LG) #Machine Learning and Algorithms #semigroups and automata theory
paper · pdf · doi:10.48550/arxiv.2307.10434
openalex publication_date 2023/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.