Abstract
Inference of RFSAs has been recently presented [1] as an alternative to inference of DFAs if the target language has been obtained by a random generation of NFAs. We propose in this paper the algorithm RPNI2, which is a variation of the previous RPNI, that also outputs DFAs as hypothesis. The experiments done using the same data as in [1] show that RPNI2 has an error rate very similar to the rate obtained in the inference of RFSAs, but the size of the hypothesis is substantially smaller.
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References
Denis, F., Lemay, A., Terlutte, A.: Learning regular languages using rfsas. Theoretical Computer Science 313, 267–294 (2004)
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García, P., Ruiz, J., Cano, A., Alvarez, G. (2006). Is Learning RFSAs Better Than Learning DFAs?. In: Farré, J., Litovsky, I., Schmitz, S. (eds) Implementation and Application of Automata. CIAA 2005. Lecture Notes in Computer Science, vol 3845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11605157_30
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DOI: https://doi.org/10.1007/11605157_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31023-5
Online ISBN: 978-3-540-33097-4
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