An Expert System Powered By Uncertainty
The Artificial Intelligence community sought to understand human intelligence by building computer programs,
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cheap abercrombie & fitch clothes, then the? Then? part of the rule was concluded. These became rule based Expert Systems. But knowledge was sometimes factual and at other times, vague. Factual knowledge had clear cause to effect relationships, where clear conclusions could be drawn from concrete rules. Pain was one symptom of a disease. If the disease always exhibited pain, then pain pointed to the disease. But vague and judgmental knowledge was called heuristic knowledge. It was more of an art. The pain symptom could not mechanically point to diseases, which occasionally exhibited pain. Uncertainty did not yield concrete answers. The AI community tried to solve this problem by suggesting a statistical, or heuristic analysis of uncertainty. The possibilities were represented by real numbers or by sets of real-valued vectors. The vectors were evaluated by means of different? fuzzy? concepts. The components of the measurements were listed, giving the basis of the numerical values. Variations were combined, using methods for computing combination of variances. The combined uncertainty and its components were expressed in the form of? standard deviations.? Uncertainty was given a mathematical expression, which was hardly useful in the diagnosis of a disease. The human mind did not compute mathematical relationships to assess uncertainty. The mind knew that a particular symptom pointed to a possibility, because it used intuition, a process of elimination, to instantly identify patterns. Vague information was powerfully useful to an elimination process, since they eliminated many other possibilities. If the patient lacked pain, all diseases, which always exhibited pain, could be eliminated. Diseases,
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