2026-07-24T05:42:08Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7695 2024-03-06T10:40:46Z ddc:004 open_access

CALF: Categorical Automata Learning Framework van Heerdt, Gerco Sammartino, Matteo Silva, Alexandra automata learning category theory 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Gerco van Heerdt and Matteo Sammartino and Alexandra Silva 2017 Is Part Of LIPIcs, Volume 82, 26th EACSL Annual Conference on Computer Science Logic (CSL 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CSL.2017.29 urn:nbn:de:0030-drops-76950 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CSL.2017.29 eng https://creativecommons.org/licenses/by/3.0/legalcode