• Expert systems are a crucial subset of artificial intelligence (AI) that simulate the decision-making ability of a human expert.
• These systems use a knowledge base filled with domain-specific information and rules to interpret and solve complex problems.
• Expert systems are widely used in fields such as medical diagnosis, accounting, coding, and even in games.

The process of building an expert system is called knowledge engineering,

where knowledge engineers ensure that the system accurately represents the required expert knowledge, stored symbolically in the system's memory to allow for effective problem-solving.

Structure of an expert system

The structure of an expert system is composed of several key components that work together

to simulate human decision-making and problem-solving. These components are essential for

storing, processing, and utilizing domain-specific knowledge to provide expert-level advice or

solutions.

1. Knowledge Base:

The knowledge base is the core component of the expert system and stores all the domain

specific knowledge necessary for problem-solving.

• It is composed of two types of knowledge:

 Factual Knowledge

 Heuristic Knowledge

2. Inference Engine:

The inference engine is the processing component that applies logical rules to the knowledge

base in order to draw conclusions or make decisions. It is the "brain" of the expert system and uses reasoning techniques to solve problems based on the input data provided by the user.

• The inference engine operates using two main reasoning methods: