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In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert.

Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code. Expert systems were among the first truly successful forms of AI software. An expert system is divided into two subsystems: 1) a knowledge base, which represents facts and rules; and 2) an inference engine, which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities.

These systems were often described as the early forms of expert systems.

This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. These expert systems in medicine were the MYCIN expert system, the Internist-I expert system and later, in the middle of the 1980s, the CADUCEUS. Expert systems were formally introduced around 1965 by the Stanford Heuristic Programming Project led by Edward Feigenbaum, who is sometimes termed the "father of expert systems"; other key early contributors were Bruce Buchanan and Randall Davis. Research on expert systems was also active in Europe. In the US, the focus tended to be on the use of production rule 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 Europe, research focused more on systems and expert systems shells developed in Prolog. One such early expert system shell based on Prolog was APES. Universities offered expert system courses and two-thirds of the Fortune 500 companies applied the technology in daily business activities. The first expert system to be used in a design capacity for a large-scale product was the Synthesis of Integral Design (SID) software program, developed in 1982. In the 1990s and beyond, the term expert system and the idea of a standalone AI system mostly dropped from the IT lexicon. One is that "expert systems failed": the IT world moved on because expert systems did not deliver on their over hyped promise. 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 being standalone tools for developing special purpose expert systems, to being one of many standard tools. Many of the leading major business application suite vendors (such as SAP, Siebel, and Oracle) integrated expert system abilities into their suite of products as a way to specify 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.

An expert system is an example of a knowledge-based system. In the simple example above if the system had used R1 to assert that Socrates was Mortal and a user wished to understand why Socrates was mortal they could query the system and the system would look back at the rules which fired to cause the assertion and present those rules to the user as an explanation.

For instance, when an expert system with 100 million rules was envisioned as the ultimate expert system, it became obvious that such system would be too complex and it would face too many computational problems. The key challenges that expert systems in medicine (if one considers computer-aided diagnostic systems as modern expert systems), and perhaps in other application domains, include issues related to aspects such as: big data, existing regulations, healthcare practice, various algorithmic issues, and system assessment.

It was one of the first medical expert systems to go into routine clinical use internationally and the first expert system to be used for diagnosis daily in Australia.

Quick Facts

• These expert systems in medicine were the MYCIN expert system, the Internist-I expert system and later, in the middle of the 1980s, the CADUCEUS.
• For instance, when an expert system with 100 million rules was envisioned as the ultimate expert system, it became obvious that such system would be too complex and it would face too many computational problems.
• An expert system is divided into two subsystems: 1) a knowledge base, which represents facts and rules; and 2) an inference engine, which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities.
• An expert system is an example of a knowledge-based system.
• Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code.

Source material: Wikipedia - "Expert system". Adapted and summarized for DiscoverScroll. Original contributors are credited through the linked Wikipedia article. Read original on Wikipedia. CC BY-SA 4.0. Changes were made from the original.