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Learning Reversible Languages with Terminal Distinguishability

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Grammatical Inference: Algorithms and Applications (ICGI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4201))

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Abstract

k-reversible languages are regular ones that offer interesting properties under the point of view of identification of formal languages in the limit. Different methods have been proposed to identify k-reversible languages in the limit from positive samples. Non-regular language classes have been reduced to regular reversible languages in order to solve their associated learning problems. In this work, we present a hierarchy of reversible languages which can be characterized by some properties related to the set of terminal segments of the automata (terminal distinguishability). Terminal distinguishability is a property that has been previously used to characterize other language families which can be identified in the limit from positive data. In the present work we combine reversibility and terminal distinguishability in order to define a new hierarchy of regular languages which is highly related to the k-reversible hierarchy. We will provide an efficient method to identify any given language in the hierarchy from only positive examples.

Work supported by the Spanish CICYT under contract TIC2003-09319-C03-02 and the Generalitat Valenciana GV06/068.

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References

  1. Angluin, D.: Inference of Reversible Languages. Journal of the Association for Computing Machinery 29(3), 741–765 (1982)

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© 2006 Springer-Verlag Berlin Heidelberg

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Sempere, J.M. (2006). Learning Reversible Languages with Terminal Distinguishability. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2006. Lecture Notes in Computer Science(), vol 4201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11872436_34

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  • DOI: https://doi.org/10.1007/11872436_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45264-5

  • Online ISBN: 978-3-540-45265-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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