Expert System Architecture in AI

Expert System Architecture in AI

Building an expert system is commonly referred to as knowledge engineering. Expert system building is typically an iterative procedure. The knowledge engineer will meet with experts and users multiple times to fine-tune the components and their interactions. 

Expert System Architecture in AI

Key components of an expert system include:

  • User Interface
  • Inference Engine
  • Explanation Facility
  • Knowledge Base
  • Knowledge Engineer

1. User Interface

Through the user interface, the expert system and the user can communicate. It serves as a link between the user and the knowledge base. The user can enter data, ask questions of the system, and get guidance from it. 

In a language that is understandable to them, and it is then transformed into a format that the expert system can comprehend. The user interface makes the dialog as vibrant and user-friendly as possible by including as many features as feasible, such as menus and a graphical user interface.

2. Inference Engine 

Since it is the primary processing unit of the system, the inference engine is referred to as the expert system's brain. It uses the knowledge base and inference rules to draw conclusions and infer new data. It assists in determining an error-free response to the user's inquiries. The system retrieves the knowledge from the knowledge base with the aid of an inference engine.

Two categories of inference engines exist 

  • Deterministic inference engine:  This kind of inference engine makes assumptions about the veracity of its conclusions. It is founded on guidelines and facts. 

  • Probabilistic inference engines:  These inference engines are based on probability and have findings that are subject to uncertainty.

The following modes are used by the inference engine to obtain the solutions:

  • Forward Chaining: It begins with the established rules and facts, then adds the conclusion to the established facts by using the inference rules.

  • Backward Chaining: This technique of reasoning backward begins with the objective and proceeds backward to support the known facts.

3. Explanation Facility

This part serves to clarify for the user how an expert system arrives at a particular choice or solution to an issue that has to be resolved. The reasoning behind a particular choice made by the expert system is available to the user. 

This part can respond to inquiries such as:

  • How are decisions made? 
  • Why is the system answering this question? 
  • Which criteria is the choice based on?

4. Knowledge Base

A knowledgebase is a kind of storage where knowledge gathered from several subject matter experts in a given field is kept. It is regarded as a large knowledge repository. The Expert System will be more accurate than the larger knowledge base. It is comparable to a database that holds data and regulations specific to a given field or topic. The knowledge base can alternatively be seen as a collection of items and their characteristics.

For example, a tiger is an object whose characteristics include being a mammal and not being a domestic animal.

Large systems of "if-then" statements make up knowledge bases, which can also only have associative associations between various concepts or be big databases of data that can be compared to one another using straightforward expert system principles.

5. Knowledge Engineer

The following processes are commonly referred to as "knowledge engineering": 

  • Designing 
  • Building 
  • Installing

A knowledge-based system, such as an expert system. Put another way, knowledge engineering includes the entire process of creating a Knowledge Base System, from start to finish. 

Extracting knowledge from the expert and representing it using the expert system shell is the responsibility of the knowledge engineer.

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