An Expert System Powered By Uncertainty

The Artificial Intelligence community sought to understand human intelligence by building computer programs, showed that intelligent behavior. Intelligence was perceived as an ability solve problems. Most human problems appeared to have reasoned, instead mathematics, solutions. The diagnosis of a disease could hardly be calculated. If a patient had a group of symptoms, and then she had a particular disease. But such reasoning required prior knowledge. The programs needed for the " " knowledge of the disease showed a group of symptoms. For the AI community, vague knowledge that resides in the minds of " Experts " was superior to text book knowledge. But they called the programs, they solve these problems, Expert Systems.
Expert Systems managed goal oriented tasks to solve problems, including diagnosis, planning, programming, configuration and design. One method of knowledge through representation was " If, then ... " rules. When the " If " part of a rule was satisfied, then the " After " of state was completed. These became rule based Expert Systems. But it was sometimes factual knowledge and in other times, vague. Factual knowledge had clear cause to effect relationships, where clear conclusions could be drawn from concrete rules. The pain was a symptom of a disease. If the disease always exhibited pain, pain, then drew attention to the disease. But vague knowledge and judgement was called heuristic knowledge. It was more of an art. The symptom pain could not mechanically point of disease, which occasionally exhibited pain. The uncertainty did not yield concrete answers.
The AI community tried to solve this problem, suggesting a statistical, or heuristics of uncertainty. The possibilities were represented by real numbers or sets of real-valued vectors. The vectors were evaluated by different " " fuzzy concepts. The components of the measurements were listed, giving the basis of numerical values. Variations were combined, using methods of computing combination of variances. The combined uncertainty and its components were cast in the form of " standard deviations. " Uncertainty was given a mathematical expression, which was hardly useful in diagnosis of a disease.
The human mind does not compute the mathematical relationships to assess the uncertainty. The mind knew that a particular symptom pointed to a possibility, since it used intuition, a process of elimination to identify patterns instantly. Vago information was powerfully useful to eliminate a process, since it eliminated many other possibilities. If the patient lacked pain, all diseases, which always exhibited pain, could be eliminated. Diseases that sometimes exhibited pain were retained. Other helped identify symptoms of a very low base. The selection was easier from a smaller group. Uncertainty may be useful for removing a powerful process.
Intuition was an algorithm, which evaluated the entire database, eliminating any context that does not fit. This algorithm has powered Expert Systems, which has acted quickly to recognize a disease, identify a case law or diagnose the problems of a complex machine. It was instant, holistic, and logical. If several parallel answers could be presented as the various parameters of a power plant, recognition was instantaneous. For the mind, where millions of parameters were presented simultaneously, in real time pattern recognition was practical. And elimination was the key, that could handle uncertainty conclusively, without recourse to abstruse calculations.



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