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Unambiguous Automata Inference by Means of State-Merging Methods

  • François Coste
  • Daniel Fredouille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

We consider inference of automata from given data. A classical problem is to find the smallest compatible automaton, i.e. the smallest automaton accepting all examples and rejecting all counter-examples. We study unambiguous automata (UFA) inference, an intermediate framework between the hard nondeterministic automata (NFA) inference and the well known deterministic automata (DFA) inference. The search space for UFA inference is described and original theoretical results on both the DFA and the UFA inference search space are given. An algorithm for UFA inference is proposed and experimental results on a benchmark with both deterministic and nondeterministic targets are provided showing that UFA inference outperforms DFA inference.

Keywords

Search Space Regular Expression Regular Language Bottom Element Deterministic Automaton 
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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • François Coste
    • 1
  • Daniel Fredouille
    • 1
  1. 1.IRISA-INRIARennes Cedex

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