From a technical perspective, the typical expert system can be divided into two essential parts: the knowledge base and the inference engine. The knowledge base contains the body of knowledge, or set of facts and relationships, obtained from the knowledge acquisition phase. The rules associated with a knowledge base tend to be heuristic and take the form of conditional statements. Whereas, the inference engine is a collection of computer routines that control the system paths through the knowledge base to enable recommendations. In addition, the inference engine serves as a bridge between the knowledge base and user.
Methodologically, the knowledge engineer defines the ambit of issues that the purposed system will address because one logic path too broad may result in a system too difficult to manage and may generate a system crash. Contrastingly, the knowledge engineer must be careful not to limit an issue too much because a logic path too narrow will produce a system so rudimentary that results will be worthless.
“View Part I of the Compliance through Automation: Expert Systems series here“