Van Heerdt, G; Sammartino, M; Silva, A; (2017) CALF: Categorical automata learning framework. In: Goranko, V and Dam, M, (eds.) Proceedings of the 26th EACSL Annual Conference on Computer Science Logic (CSL 2017). (pp. 29:1-29:24). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik: Dagstuhl, Germany.

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Abstract

Automata learning is a technique that has successfully been applied in verification, with the automaton type varying depending on the application domain. Adaptations of automata learning algorithms for increasingly complex types of automata have to be developed from scratch because there was no abstract theory offering guidelines. This makes it hard to devise such algorithms, and it obscures their correctness proofs. We introduce a simple category-theoretic formalism that provides an appropriately abstract foundation for studying automata learning. Furthermore, our framework establishes formal relations between algorithms for learning, testing, and minimization. We illustrate its generality with two examples: deterministic and weighted automata.

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