Abstract
Recently, Augmented Regular Expressions (AREs) have been proposed as a formalism to describe and recognize a non-trivial class of context-sensitive languages (CSLs), that covers planar shapes with symmetries [1, 2]. AREs augment the expressive power of Regular Expressions (REs) by including a set of constraints, that involve the number of instances in a string of the operands of the star operations of an RE. A general method to infer AREs from string examples has been reported [2] that is based on a regular grammatical inference (RGI) step followed by a constraint induction process. This approach avoids the difficulty of learning context-sensitive grammars. In this paper, a specific method for learning AREs from positive examples is described, in which the RGI step is carried out by training a recurrent neural network for a prediction task [3] and extracting a DFA from the network dynamics [4]. The ARE learning method has been applied to the inference of a set of eight test CSLs, and good experimental results have been obtained.
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© 1996 Springer-Verlag Berlin Heidelberg
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Alquézar, R., Sanfeliu, A., Cueva, J. (1996). Learning of context-sensitive language acceptors through regular inference and constraint induction. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033349
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DOI: https://doi.org/10.1007/BFb0033349
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