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E-book InformationAn Expert System Battery-powered By Uncertainty
by:
Abraham Thomas
The Artificial Intelligence community sought-after to understand human intelligence by building computer programs, which exhibited intelligent behavior. Intelligence was perceived to be a problem resolution ability. Most human problems appeared to have reasoned, rather than mathematical, solutions. The diagnosing of a illness could hardly be calculated. If a patient had a group of symptoms, then she had a particular disease. But, such reasoning required prior knowledge. The programs needful to have the “knowledge” that the illness exhibited a particular group of symptoms. For the AI community, that vague cognition residing in the minds of “Experts” was superior to text book knowledge. So they called the programs, which resolved
such problems, Expert Systems.
Expert Systems managed goal adjusted problem resolution tasks including diagnosis, planning, scheduling, configuration and design. One know-how
of cognition representation was through “If, then...” rules. Once
the “If” part of a rule was satisfied, then the “Then” part of the rule was concluded. These became rule based Expert Systems. But cognition was sometimes factual and at another times, vague. Factual cognition had clean cause to effect relationships, wherever
clean conclusions could be drawn from concrete rules. Pain was one symptom of a disease. If the illness always exhibited pain, then pain pointed to the disease. But vague and judgmental cognition was called heuristic knowledge. It was much of an art. The pain symptom could not automatically
point to diseases, which on occasion 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 delineated by real amount or by sets of real-valued vectors. The vectors were evaluated by means of some “fuzzy” concepts. The components of the measurements were listed, giving the basis of the numerical values. Variations were combined, exploitation 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 diagnosing of a disease.
The human mind did not calculate 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 galore another possibilities. If the patient lacked pain, all diseases, which always exhibited pain, could be eliminated. Diseases, which sometimes exhibited pain were retained. Further symptoms helped identification from a greatly reduced database. A selection was easier from a smaller group. Uncertainty could be powerfully useful for an elimination process.
Intuition was an algorithm, which evaluated the whole database, eliminating every context that did not fit. This algorithmic rule has battery-powered Expert Systems which 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 some parallel answers could be presented, as in the multiple parameters of a power plant, recognition was instant. For the mind, wherever
millions of parameters were at the same time
presented, real time pattern recognition was practical. And elimination was the key, which could once and for all
handle uncertainty, without resort to deep calculations.
Just about the author:
Ibrahim Thomas is the author of The Intuitive Algorithm, a book, which suggests that intuition is a pattern recognition algorithm. The ebook version is accessible at http://www.intuition.co.in.The book may be purchased only in India. The website, provides a free film and a walk through to explain the ideas.
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