What are the primary components of an expert system?

An expert system is designed to mimic the decision-making abilities of a human expert in a specific domain. The primary components that make up an expert system are the inference engine, knowledge base, and working memory.

The knowledge base serves as the storage area for all the domain-specific knowledge that the expert system utilizes. This includes facts, rules, and heuristics that encompass the expertise required to solve problems or make decisions. Without a well-structured knowledge base, the system lacks the information necessary to function effectively.

The inference engine is the core processing component of the expert system. It applies logical rules to the knowledge base to deduce new information and make decisions based on the data provided. Essentially, it is responsible for reasoning through the information available and drawing conclusions, functioning similarly to how a human expert would analyze a situation.

Working memory acts as a temporary storage area that holds information relevant to the current problem being solved. It allows the inference engine to keep track of which facts and rules are being applied while the system is in operation.

The other options listed refer to components of different types of systems. Input, processing, and output describe a general model of computing systems but do not specifically relate to expert systems. User interface, database, and reporting system pertain more