On the inference of canonical context-free grammars
ACM SIGACT News
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Reversible automata and induction of the English auxiliary system
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
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Inductive inference is a learning process based on discovering models for bodies of knowledge, given sample information. The inference process we discuss here is concerned with inductive acquisition of syntactic models for context-free languages (CFLs), given appropriate language samples. The knowledge to be modeled in this case is any CFL L, with the model to be determined a recognitive or generative characterization of L's syntactic structure. L will be learned syntactically once a machine M recognizing L, or a context-free grammar (CFG) G generating L, is inductively inferred from a sentence sample. The capability of distinguishing between L and its complement, or of generating all and only L's sentences, is the knowledge acquired, with the learner (inference process) gaining this knowledge by acquiring M or G. An observer (informant, teacher, or oracle) has such knowledge of L and can provide the learner with appropriate sample information to ensure that M or G is correctly identified.