What are the components of an expert system?

Overview

An expert system uses facts and knowledge to solve complicated problems of a specific domain. One expert system has expertise only in one field.

Components of an expert system

The expert system in artificial intelligence consists of the following components:

• User interface

• Explanation facility

• Inference engine

• Knowledge base

• Knowledge base acquisition facility

User interface (UI)

Users interact with interactive UI. The UI will take all the parameters and pass the information to the inference engine. After computing the result from the user's input, UI then shows the result on the screen.

Inference engine

The mind of the expert system has all the predefined rules to use the information from the knowledge base. It takes user input queries from the user interface and applies knowledge facts and rules. It can also deduce the problem to find a solution. Two strategies are used to find the answer to the problem:

• Forward chaining: It uses rules of inference and data to deduce more rules until it comes to a conclusion. And adds new information to its database for future problems. It moves its reasoning toward the goal. Its approach can be called data-driven reasoning.

• Backward chaining: It works its way back from the goal. So basically, it does the backtracking from the goal state to the present state. It is goal-driven reasoning. As the goal is already known, it becomes easy to use rules of inference to find the solution.

Knowledge base

It is the database where experts' knowledge is stored. Knowledge engineers gather knowledge from the experts and keep it in the knowledge base. Knowledge can be heuristic or factual:

• Heuristic knowledge is disordered information in nature.

• Factual knowledge is based on facts that are proven and widely accepted.

Explanation base

It is the optional component that shows the user the explanation of the solution. It shows the reason why it suggests the proposed solution.

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