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An expert system is an example of a knowledge-based system. Expert systems were the first commercial systems to use a knowledge-based architecture. In general view, an expert system includes the following components: a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface. [48] [49]
expert: describes only the task the system is designed for – its purpose is to aid replace a human expert in a task typically requiring specialised knowledge; knowledge-based: refers only to the system's architecture – it represents knowledge explicitly, rather than as procedural code
These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems. [1] One of the first and most popular of such methodologies custom designed for expert systems was the Knowledge Acquisition and Documentation Structuring (KADS) methodology developed in Europe ...
In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving. [53] The simplest approach for an expert system knowledge base is simply a collection or network of production rules. Production rules connect symbols in a relationship similar to an If-Then statement.
A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. [1] For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game.
Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in expert systems. For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.
Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. [1] Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers ...