Expert system

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{theory, work, human}
{system, computer, user}
{math, number, function}
{law, state, case}
{company, market, business}
{game, team, player}
{disease, patient, cell}
{specie, animal, plant}
{food, make, wine}
{car, race, vehicle}
{rate, high, increase}

An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and are a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert; however, common to most or all are: 1) the creation of a knowledge base which uses some knowledge representation structure to capture the knowledge of the Subject Matter Expert (SME); 2) a process of gathering that knowledge from the SME and codifying it according to the structure, which is called knowledge engineering; and 3) once the system is developed, it is placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or as a supplement to some information system. Expert systems may or may not have learning components.

Expert systems were introduced by researchers in the Stanford Heuristic Programming Project, including the "father of expert systems" Edward Feigenbaum, with the Dendral and Mycin systems. Principal contributors to the technology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, Carli Scott, and others at Stanford. Expert systems were among the first truly successful forms of AI software. [1] [2] [3] [4] [5] [6]

The topic of expert systems also has connections to general systems theory, operations research, business process reengineering, and various topics in applied mathematics and management science.

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