Abstract
We define here the Residual Finite State Automata class (RFSA). This class, included in the Non deterministic Finite Automata class, strictly contains the Deterministic Finite Automata class and shares with it a fundamental property : the existence of a canonical minimal form for any regular language. We also define a notion of characteristic sample S L for a given regular language L and a learning algorithm (DeLeTe). We show that DeLeTe can produce the canonical RFSA of a regular language L from any sample S which contains S L . We think that working on non deterministic automata will allow, in a great amount of cases, to reduce the size of the characteristic sample. This is already true for some languages for which the sample needed by DeLete is far smaller than the one needed by classical algorithms.
This work was partially supported by “Motricité et Cognition : Contrat par objectif région Nord/Pas-de-Calais”
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Denis, F., Lemay, A., Terlutte, A. (2000). Learning Regular Languages Using Non Deterministic Finite Automata. In: Oliveira, A.L. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2000. Lecture Notes in Computer Science(), vol 1891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45257-7_4
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DOI: https://doi.org/10.1007/978-3-540-45257-7_4
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