Patterns of Induction and Associated Knowledge Acquisition Algorithms,
CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
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The common need of both Artificial Intelligence and Pattern Recognition for effective methods of automatic knowledge acquisition is considered. A pattern of induction is defined as a framework which relates a theory of behavior generation, underlying knowledge structures, and a learning methodology. One particular learning theory, called interference matching, suggests that knowledge structures which underlie behavior descriptions can be directly abstracted from those descriptions. Because of the close connection between descriptions and inferences in such a framework, the strengths and weaknesses of several types of descriptions are considered. Algorithms which exploit this theory are presented for three classes of problems pattern learning and classification induction of quantified production rules and the induction of syntactic categories and phrase structure rules. Preliminary results are presented and directions for future research are outlined.