Learning Context-Sensitive Languages from Linear Structural Information
نویسنده
چکیده
In this work we propose a method to infer context-sensitive languages from positive structural examples produced by linear grammars. Our approach is based on a representation theorem induced by two operations over strings: duplication and reversal. The inference method produces an acceptor device which is an unconventional model of computation based on biomolecules (DNA computing). We prove that a subclass of context-sensitive languages can be inferred by using the representation result in combination with reductions from linear languages to k-testable in the strict sense regular languages.
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