Abstract
This paper presents algorithms for inductive refinement of production rules based on the DLG data-driven machine learning algorithm. These algorithms modify the input production rules with reference to a set of examples so as to ensure that all positive examples are covered and no negative examples are covered. The input production rules may either have been previously learnt by a machine learning system or be extracted from an existing expert system.
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