Automata Learning: A Categorical Perspective

Silva A, Jacobs B.  2014.  Automata Learning: A Categorical Perspective. Horizons of the Mind. A Tribute to Prakash Panangaden - Lecture Notes in Computer Science. 8464


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.

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