Integration of Deductive Learning and Inductive Learning by Extended EBL

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The objective of this research was to establish a methodology for acquiring valid and useful macro rules under the imperfect domain theory.The following results were obtained1)proposition of an extended EBLThe augmented EBL is a framework for knowledge refinement based on generalization from prural examples. As operationality criteria, the maximization of the usage and the minimization of backtracking number were introduced. The least EBG is useful to find operational generalizations incrementally. A Learning system with an incremental least EBG generator has been realized.2)an approach to the inconsistent problemA method for solving the inconsistent problem has been proposed by introducing a concept of maximal covering. By top down search over generalization hierarchy of common explanation structures, the best set of macro rules which includes all positive examples and excludes all negative ones can be found.3)an approach to the intractable problemFocusing an a class of problem solving that have serially desomposable subgoals, an augmented EBL learner that acquires a problem solving macrotable from examples. The usefulness of the learner has been shown by applying to the eight puzzle problem.