Automata learning is a known technique to infer a finite state machine from a set of observations. In this paper, we revisit Angluin's original algorithm from a categorical perspective. This abstract view on the main ingredients of the algorithm lays a uniform framework to derive algorithms for other types of automata. We show a straightforward generalization to Moore and Mealy machines, which yields an algorithm already know in the literature, and we discuss generalizations to other types of automata, including weighted automata. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Jacobs, B., & Silva, A. (2014). Automata learning: A categorical perspective. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8464 LNCS, pp. 384–406). Springer Verlag. https://doi.org/10.1007/978-3-319-06880-0_20
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