The Components of The Expert Systems in AI

- Overview

An expert system (ES) in artificial intelligence (AI) is a computer program that uses AI technologies to mimic the decision-making and problem-solving abilities of a human expert in a specific field. ESs are designed to solve complex problems by reasoning through knowledge bases, which are represented as if–then rules, rather than procedural code.

ESs are intended to complement, rather than replace, human experts, and can offer consistent decision-making processes that eliminate bias and improve the quality of outcomes. 

The ES in AI has five components:

• Knowledge Base: The knowledge base contains the facts and rules in the expert system. It includes the specification of problem solving and the development of methods related to the domain and knowledge of a particular discipline.
• Inference engine: The most basic job of the inference engine is to collect relevant information from the knowledge base, analyze it, and determine solutions to user problems. The inference engine also has interpretation and troubleshooting capabilities.
• Explanation module: This module helps the expert system to give the user an explanation about how the expert system reached a particular conclusion.
• Knowledge acquisition and learning module: With the help of this component, the expert system can collect more information from many sources. Afterwards, the knowledge is stored in the knowledge base.
• User interface: This element allows non-expert users to communicate with the expert system and develop solutions.