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1960s - 1970s: Expert Systems Gain Attendance |
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The concept of  expert systems dates back to the 1960s but first gained prominence in the 1970s. Conclusive for this development were the insights of the Stanford University professor Edward Feigenbaum, who in 1977 demonstrated that the problem-solving capacity of a computer program rather is a result of the knowledge it posses, than of the applied programming techniques and formalisms.
Expert systems were designed to mimic the knowledge and reasoning capabilities of a human specialist in a given domain by using (top down) artificial intelligence techniques. Made possible by the large storage capacity of the computers at the time, expert systems had the potential to interpret statistics and formulate rules. An initial use of expert systems was to diagnose and treat human physical disorders, but as its applications in the market place were extensive over the course of the following years they were also employed in fields such as stock market forecast, taxation, chemistry, and geology.
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Expert system
Expert systems are advanced computer programs that mimic the knowledge and reasoning capabilities of an expert in a particular discipline. Their creators strive to clone the expertise of one or several human specialists to develop a tool that can be used by the layman to solve difficult or ambiguous problems. Expert systems differ from conventional computer programs as they combine facts with rules that state relations between the facts to achieve a crude form of reasoning analogous to artificial intelligence. The three main elements of expert systems are: (1) an interface which allows interaction between the system and the user, (2) a database (also called the knowledge base) which consists of axioms and rules, and (3) the inference engine, a computer program that executes the inference-making process. The disadvantage of rule-based expert systems is that they cannot handle unanticipated events, as every condition that may be encountered must be described by a rule. They also remain limited to narrow problem domains such as troubleshooting malfunctioning equipment or medical image interpretation, but still have the advantage of being much lower in costs compared with paying an expert or a team of specialists.
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