2019/02/15 by Simone Barlocco, Barlocco, Simone, Clemens Kupke +3
Computer Science · #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Logic, programming, and type systems #Machine Learning and Algorithms #semigroups and automata theory
paper · pdf · doi:10.48550/arxiv.1902.05762
openalex publication_date 2019/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination.