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Building Knowledge Repositories with Neural Networks and Orthogonal Designed Bases of Expert Holistic Judgment
This paper proposes and illustrates a framework of building knowledge repositories with Neural Expert Systems (NES) to overcome difficulties often encountered by conventional experts systems technologies. The proposed system uses Neural Networks (NN) to capture decision patterns / production rules in a set of holistic judgments provided by experts on a minimal sample of cases taken from a problem domain. A NN learns tacit knowledge from implicit relationships between decision attributes and outcome. Holistic judgments overcome the difficulty of explaining explicitly production rules and heuristics of the experts. An orthogonal plan defines a minimal sample to acquire the initial training set for NN and to alleviate experts from the cognitive burden of specifying a complete set of production rules. Starting from this initial knowledge base, counter-examples given by experts when the system is in production are added to subsequent training. The knowledge base of a NES and consequently the knowledge repository will grow over time with additional patterns learning from its own production and expert opinions.
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