The Expert system is developed to resolve complex problems and to generate decision-making abilities like a human expert of a specialized field. This induced ability of this system to extract information from its knowledge base using the logic and conclusion rules according to the user queries.
The expert systems are parts of Artificial Intelligence, and the first Expert System was invented in the year 1970, which was the initial successful move toward artificial intelligence. It resolves the most complex question as an expert by extracting the knowledge stored in its knowledge base.
The effectiveness of an expert system is based on the proficient knowledge stored in its knowledge base. The more information stored in the KB, the more that the system enhanced would be its performance. A common example of an Expert System is a correction of spelling mistakes while typing in the Google search box.
The block diagram symbolizes the operations and working of an expert system:

Example s of Expert Systems

Expert systems are used in many fields there are some common examples of Expert systems:

DENDRAL

It was a chemical analysis expert system. It was used in organic chemistry to identify the unidentified organic molecules with the help of their masses and knowledge base of chemistry.

MYCIN

It was developed to find the bacteria and their species causing infections like bacteremia and meningitis. It was also utilized for the suggestion of antibiotics and the analysis of blood clotting diseases.

PXDES

It is an expert system developed to detect the level of lung cancer. To make a decision about the disease, it acquires a picture from the upper body, which looks like the shadow. This shadow recognizes the type and level of damage.

CaDeT

The CaDet expert system is an indicative support system that can detect cancer at early stages.

Characteristics of Expert System

There are the following characteristics of the Expert system.

High Performance

The expert system makes available high performance for resolving any type of complex problem of a precise domain with high competence and accuracy.

Understandable

The expert system responses are easily understandable to the user. It can obtain input in human language and make available the output in the same way.

Reliable

It is much dependable for creating and producing an efficient and accurate output.

Highly responsive

Expert systems present the outcome for any complex query inside a very short period of time.

Components of Expert System

An expert system has three primary components by the combination of these parts an Expert system produces outputs mainly consists of three components:

  • User Interface
  • Inference Engine
  • Knowledge Base

User Interface

The expert system communicates with the user by using the user interface. It gets queries as input in a readable format and transfers that information to the inference engine. After receiving the reply from the inference engine, it provides the output to the user. In other words, it is an interface that helps a non-expert user to correspond with the expert system to find a solution.

Inference Engine

It is the brain of the expert system as it is the main processing unit of the system. It provides inference rules to the knowledge base to draw from a conclusion or construe new information. It assists in obtaining a mistake-free solution to queries inquired by the user.

Knowledge Base

It is a pile of related information that stores knowledge obtained from the different experts of the particular area. It is well-thought-out big storage of knowledge. The more the knowledge base, the further accurate will be the Expert System.

The development of Expert System

Expert
The attainment of an Expert System much depends on the knowledge made available by human experts. These experts are those persons who are dedicated to that specific area and field.

Knowledge Engineer

A knowledge engineer is an expert who collects and evaluates the knowledge from the field of experts and then codifies that information to the system according to formalism.

End-User

This end user is a person who may not be specialists, and functioning on the expert system wants the solution or recommendation for his queries, which are complex.

Advantages Expert System

Before using any technology we don’t exactly familiar with its benefits like Electricity, Internet, Mobile phones, etc.
Why we need to build up a computer-based system. So below are the points that are unfolding the need of the Expert System.

No memory Limitations

It can accumulate as much data as necessary and can memorize it at the time of its submission. But for human experts, there are some boundaries to remember all things at every time.

High Efficiency

If the knowledge base is restructured with accurate knowledge, then it produces a highly efficient output, which may not be achievable for a human.

Expertise in a domain

There are plenty of person specialists in each field, and they all have different abilities, different experiences, and different skills, so it is not easy to get a last output for the query. If we place the information gained from a human specialist from a specific domain into the expert system, then it gives an efficient output by integrating all the facts and knowledge.

Not affected by emotions

The expert systems not affected by human emotions such as exhaustion, irritation, despair, nervousness, etc. Hence the presentation remains constant.

High security

These systems make available high safety and security to resolve any query.

Considers all the facts

The expert system makes sure and considers all the accessible facts and presents the result, therefore. But it is probable that a human specialist may not consider some facts due to any reason.

Regular updates improve the performance

If there is an issue in the outcome provided by the expert systems, we can get better the presentation of the system by updating the knowledge base.

Limitations of Expert System

  • The answer of the expert system may get wrong if the knowledge base enclosed the wrong information.
  • It cannot reproduce an imaginative output for different scenarios.
  • Its preservation and maturity costs are very high.
  • information acquisition for designing is much difficult.
  • For each area and field, we need a specific ES, which is one of the big limitations.
  • It cannot become skilled at itself and hence necessitate manual updates.