One of the most successful applications of artificial intelligence reasoning techniques using facts and rules has been in building expert systems that embody knowledge about a specialized field of human endeavour, such as medicine, engineering, or business. Knowledge Base and the Inference Engine are two major parts of the expert system. The knowledge base consists of facts and rules about the subject at hand. The inference engine consists of all processes that manipulate the knowledge base to deduce information requested by the user.

Knowledge acquisition is a crucial stage in the development of expert systems. As a process, it involves eliciting, interpreting and representing the knowledge from a given domain. Knowledge acquisition for expert systems (from domain experts) is time consuming, expensive and potentially unreliable.

In the construction of an expert system, a ‘knowledge engineer’ (usually a computer scientist with artificial intelligence training) works with an expert (or experts) in the field of application in order. to represent the relevant knowledge of the expert in a form that can be entered into the knowledge base. This process is often aided by a knowledge acquisition subsystem that, among other things, checks the growing knowledge base for possible inconsistencies and incomplete information. These are then presented to the expert for resolution.

Book: Expert Systems: The Technology of Knowledge Management for the 21st Century, by: Cornelius T. Leondes.
Web Resources: Expert Systems and Artificial Engineering
Power Point Presentation (PPT): Knowledge Engineering and Expert Systems

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