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Introducing Domain and Typing Bias in Automata Inference

  • François Coste
  • Daniel Fredouille
  • Christopher Kermorvant
  • Colin de la Higuera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3264)

Abstract

Grammatical inference consists in learning formal grammars for unknown languages when given sequential learning data. Classically this data is raw: Strings that belong to the language and eventually strings that do not. In this paper, we present a generic setting allowing to express domain and typing background knowledge. Algorithmic solutions are provided to introduce this additional information efficiently in the classical state-merging automata learning framework. Improvement induced by the use of this background knowledge is shown on both artificial and real data.

Keywords

Automata Inference Background Knowledge 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • François Coste
    • 1
  • Daniel Fredouille
    • 2
  • Christopher Kermorvant
    • 3
  • Colin de la Higuera
    • 4
  1. 1.IRISARennesFrance
  2. 2.The Robert Gordon UniversityAberdeenUK
  3. 3.Dept. IROUniversité de MontréalCanada
  4. 4.EURISEUniversité Jean MonnetSt EtienneFrance

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