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Automata Learning: A Categorical Perspective

  • Bart Jacobs
  • Alexandra Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8464)

Abstract

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.

Keywords

Linear Setting Automaton Learn Observation Table Deterministic Automaton Mealy Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bart Jacobs
    • 1
  • Alexandra Silva
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenNetherlands

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