Abstract
This paper addresses the problem of modeling of expert knowledge as a starting point for inference analysis in uncertain knowledge-based systems. The experts' opinions in a given problem are viewed as additional information in cognitive decision processes. Depending upon which uncertainty measures are used in expert knowledge representation, different inferential engines will be proposed. The flow from data to decisions will be examined in order to help the design of intelligent systems. In considering various types of uncertainty measures, the problem of admissibility will be addressed.
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