<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandra Silva</style></author><author><style face="normal" font="default" size="100%">Bart Jacobs</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automata Learning: A Categorical Perspective</style></title><secondary-title><style face="normal" font="default" size="100%">Horizons of the Mind. A Tribute to Prakash Panangaden - Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8464</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
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