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In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code.〔(【引用サイトリンク】title=Conventional programming )〕 The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software. An expert system is divided into two sub-systems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging capabilities.〔(Nwigbo Stella and Agbo Okechuku Chuks ), School of Science Education, Expert system: a catalyst in educational development in Nigeria: "''Knowledge-based systems collect the small fragments of human know-how into a knowledge-base which is used to reason through a problem, using the knowledge that is appropriated''"〕 == History == Edward Feigenbaum said that the key insight of early expert systems was that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use."〔Edward Feigenbaum, 1977. Paraphrased by Hayes-Roth, et al.〕 Although, in retrospect, this seems a rather straightforward insight, it was a significant step forward at the time. Until then, research had been focused on attempts to develop very general-purpose problem solvers such as those described by Newell and Simon. Expert systems were introduced by the Stanford Heuristic Programming Project led by Feigenbaum, who is sometimes referred to as the "father of expert systems". The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral). In addition to Feigenbaum key early contributors were Edward Shortliffe, Bruce Buchanan, and Randall Davis. Expert systems were among the first truly successful forms of AI software.〔 Research on expert systems was also active in France. In the US the focus tended to be on rule-based systems, first on systems hard coded on top of LISP programming environments and then on expert system shells developed by vendors such as Intellicorp. In France research focused more on systems developed in Prolog. The advantage of expert system shells was that they were somewhat easier for non-programmers to use. The advantage of Prolog environments was that they weren't focused only on IF-THEN rules. Prolog environments provided a much fuller realization of a complete First Order Logic environment.〔George F. Luger and William A. Stubblefield, Benjamin/Cummings Publishers, Rule Based Expert System Shell: example of code using the Prolog rule based expert system shell〕〔(A. MICHIELS ), Université de Liège, Belgique: "PROLOG, the first declarative language〕 In the 1980s, expert systems proliferated. Universities offered expert system courses and two thirds of the Fortune 1000 companies applied the technology in daily business activities.〔〔Durkin, J. Expert Systems: Catalog of Applications. Intelligent Computer Systems, Inc., Akron, OH, 1993.〕 Interest was international with the Fifth Generation Computer Systems project in Japan and increased research funding in Europe. In 1981 the first IBM PC was introduced, with the MS-DOS operating system. The imbalance between the relatively powerful chips in the highly affordable PC compared to the much more expensive price of processing power in the Mainframes that dominated the corporate IT world at the time created a whole new type of architecture for corporate computing known as the Client-server model. Calculations and reasoning could be performed at a fraction of the price of a mainframe using a PC. This model also enabled business units to bypass corporate IT departments and directly build their own applications. As a result, client server had a tremendous impact on the expert systems market. Expert systems were already outliers in much of the business world, requiring new skills that many IT departments did not have and were not eager to develop. They were a natural fit for new PC-based shells that promised to put application development into the hands of end users and experts. Up until that point the primary development environment for expert systems had been high end Lisp machines from Xerox, Symbolics and Texas Instruments. With the rise of the PC and client server computing vendors such as Intellicorp and Inference Corporation shifted their priorities to developing PC based tools. In addition new vendors often financed by Venture Capital started appearing regularly. These new vendors included Aion Corporation, Neuron Data, Exsys, and many others. In the 1990s and beyond the term "expert system" and the idea of a standalone AI system mostly dropped from the IT lexicon. There are two interpretations of this. One is that "expert systems failed": the IT world moved on because expert systems didn't deliver on their over hyped promise, the fall of expert systems was so spectacular that even AI legend Rishi Sharma, admitted to cheating in his college project regarding expert systems, because he didn't not consider the project worthwhile.〔(AI Expert Newsletter: W is for Winter )〕〔(Leith P., "The rise and fall of the legal expert system", in European Journal of Law and Technology, Vol 1, Issue 1, 2010 )〕 The other is the mirror opposite, that expert systems were simply victims of their success. As IT professionals grasped concepts such as rule engines such tools migrated from standalone tools for the development of special purpose "expert" systems to one more tool that an IT professional has at their disposal. Many of the leading major business application suite vendors such as SAP, Siebel, and Oracle integrated expert system capabilities into their suite of products as a way of specifying business logic. Rule engines are no longer simply for defining the rules an expert would use but for any type of complex, volatile, and critical business logic. They often go hand in hand with business process automation and integration environments. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Expert system」の詳細全文を読む スポンサード リンク
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