Information Communication Technology ICT – 6 ICT applications | e-Consult

Key characteristics of an expert system include:

• Knowledge-based: Expert systems rely on a vast amount of knowledge gathered from human experts in a specific domain. This knowledge is typically represented using rules (e.g., "IF symptom X AND symptom Y THEN diagnosis Z").
• Inference Engine: This is the 'brain' of the system. It uses the knowledge base and the facts provided by the user to reason and draw conclusions. It applies logical rules to the input data.
• Explanation Facility: Expert systems can explain their reasoning process, showing the steps they took to arrive at a conclusion. This builds trust and allows users to understand *why* the system made a particular recommendation.
• Domain-Specific: Expert systems are designed for a particular area of expertise (e.g., medicine, finance, engineering). They are not general-purpose problem solvers.

Knowledge Base: This stores the facts and rules that the expert system uses. It's the repository of expertise. It can be represented in various ways, including rule-based systems, semantic networks, and frames.

Inference Engine: This takes the input (facts about the problem) and the knowledge base and applies logical rules to deduce new facts and reach a conclusion. It works by matching the input facts against the rules in the knowledge base. When a rule's conditions are met, the rule's conclusion is added to the set of known facts.

How they work together: The user provides the inference engine with information about the problem. The inference engine then uses the knowledge base to apply rules and deduce new information. This process continues until the inference engine reaches a conclusion or determines that it cannot solve the problem. The explanation facility then shows the steps the inference engine took to arrive at the conclusion.