Using artificial intelligence and machine learning we have made the subject of automation in the boom. Various systems are available to make our life easier and comfortable. Also, we have made machines predict out of the box more accurately than humans. Expert system is such a domain where the machine is taking the place of the human for making the decision using its various features. In this article, we are going to have an overview of the Expert System in detail. We will have an in-depth understanding of expert systems with their components, working, and advantages. The major points to be covered in this article are listed below.

Table of Contents 

1. What is an Expert System?
2. The Three C’s of the Expert System
3. Strategies in the Inference Engine
4. Types of Expert Systems
5. Examples of Expert Systems
6. Applications of Expert Systems 
7. Advantages of the Expert System

What is an Expert System?

In computer science, a system or program which can learn about a particular field from the knowledge base and can simulate the behaviour and decisions of a human or an organization can be considered as the expert system.

Mostly these systems use artificial intelligence to complete its task. And the data in the knowledge base is fed by the humans who are experts in the field for which the system is implemented. Mostly the users of the system are not experts in the field and using the system they gain information about the system.  There are various domains where these systems are being used frequently like medical, gaming, research, and development, etc. 

If we go into more deep details of the system we find that using the artificial intelligence and knowledge base the system helps in solving many problems of the field for which it is implemented. And the task of the human in the generation of the system is to add the data or knowledge to the knowledge base. So the system can solve complex problems of a particular field using the knowledge given by the expertise of the domain on a similar level of human intelligence and expertise.

In the field of computer science, whenever a system gets generated, we make assumptions about the characteristics and capabilities of the system. Which can be fulfilled by planning its components. Which we call three c of the system. When it comes to the expert system we can say that it should have the following characteristics, capabilities, and components.

The Three C’s of the Expert System

Characteristics

• The system should be easy to use and understand for the users.
• The results of the system should be highly reliable.
• The system should be high performing and responsive

Capabilities 

• The system should be capable of giving advice.
• They should provide assistance to humans in making decisions.
• They should be capable of diagnosing and resolving the problems.
•  The results from the system should justify the conclusion of the system.
• The system should have the capability of finding an alternative solution to a problem.

Components 

• Knowledge base: as we have discussed knowledge base is a container of the knowledge or information about the domain. Going on deeper side information in the knowledge base can be divided into two categories: facts and rules. Where facts are the information about the domain and the rules in the knowledge base can be considered as the way to solve the problems assigned to the domain.  

• Inference Engine: In the expert system the inference engine can be considered as the heart of the system. Because the main aim of the inference engine in an expert system is to circulate the queries from the user to the knowledge base and the solution of the queries from the knowledge base to the user. Mostly according to the queries it extracts relevant data from the knowledge base and makes the solution and circulates it further to the user. It can also be capable of debugging problems in the system. 

• Knowledge gainer: this component of the system helps the system to gain more knowledge about the domain from various sources and store the information in the knowledge base.

• User interface and explanation module: it is a combination of two components where the user interface helps the user to ask queries and the explanation module gives the solution to the user in an explanatory way so that the user can conclude in an easy manner. 

The below image is a representation of the workflow diagram of any basic Expert System. 

As we have discussed before the inference engine, is the heart of the Expert system. So it becomes our main focused component of the system. The work of the inference engine revolves around the two main strategies. Which we are discussing in the next section.

Strategies in the Inference Engine

Strategies used by the inference engine to provide or advise the solution can be divided into two main categories.

• Forward Chaining 
• Backward Chaining

Forward Chaining 

Using this chaining strategy the inference engine provides an optimal solution. Basically, this strategy provides the solution after iterating over all possible solutions by taking all of the facts into the account presented in the knowledge base. Forward chaining starts from the stored facts and after digging down in the set of the rules it chooses all those rules whose premises are satisfied and conclude to the known fact. And this process keeps working in a cyclic nature until a query doesn’t get a solution or advice. 

It can be considered as an information-driven or data-driven process because to make the solution it goes through the data stored into the knowledge base. A basic example of forwarding chaining can be the time series forecasting of weather where the changes in any of the variables like humidity, airflow makes the changes in the prediction of weather conditions. 

The below diagram is a representation of the forward chaining.

Backward Chaining

Unlike forward chaining, backward chaining works with the aim of generating the facts. The name of the algorithm is backward chaining because it starts with the solution and works in the backward direction to the knowledge base. The result of the algorithm is always based on the result of the forward chaining. By simplifying it we can say it works and find the answer to the question “ why did this happen?  

The main goal of the algorithm is to divide the solution into the sub solution and the aim of the sub-goal is to prove the fact is true. We can say that it is a solution-driven or goal-driven approach because the set of solutions makes the conclusion about which rule is selected for making the upcoming solution.

The below image is a representation of the backward chaining.

Types of Expert Systems

On the basis of knowledge base and frame, and logic in the algorithm we can say there can be 5 types.

•  Rule-based expert system:  In this type of system the set of rules can be considered as the knowledge base.
• Fuzzy logic expert system: In this type of system the solution of the problem is given by making a difference between the members of the class from the members who are not part of the class.
• Frame-based expert system: This type of system stores and share the data using the frame structure.
• Neural expert system: Wights of neurons are used for storing the information more formally we can say such a kind of system uses the knowledge base based on the neural network knowledge base system. 
• Neuro-fuzzy expert system: This type of system is a combination of the neural expert system and the fuzzy logic expert system which makes the calculation using the fuzzy logic and storage of the information using the weights of neurons.  

Examples of Expert Systems

There are various domains where we can see the use of the export system. Some of the examples of expert systems are as follows.

• SHINE: This system is designed for analyzing and monitoring the real-time or non-real-time systems which are designed by NASA.
• MUDMAN: MUDMAN is an expert system that helps the on-site engineer do the job more consistently designed by N L Baroid Company.
• PROSPECTOR: PROSPECTOR helps the United States in geological survey and exploration of geological minerals. Designed by the SRI’s Artificial Intelligence Center.
• XCON: XCON is a rule-based expert system that is designed to automatically select the computer system components based on the customer’s requirements.
• CaDet: CaDet is an Expert System that is designed to help in identifying cancer at early stages.

Applications of Expert Systems 

As of now, we have seen insights into the expert system and various expert systems. In this section of the article, we are going to discuss some of the main domains where we can use the these system. 

• Medical domain: as we know this domain is one of the most difficult domains for the user to understand and the main focus of the expert system is to provide a solution with a well-defined conclusion so that the user won’t get confused about the final advice or solution. Also, there are various expert systems that have been generated to work like disease diagnosis, medical operations on humans and animals.

• Manufacturing/Process control system: in various industries we can observe expert systems are installed for controlling the processes under manufacturing or physical processes units.

• Research domain: we can also apply the expert system in the field of r&d where it can help in debugging the faults with the provision of results.

• Financial and economical domain: as we talked about forwarding chaining we can conclude that using these algorithms we can allow a share market system to predict the ups and downs in the market. Also, these systems can help in fraud detection if we talk about banking and finance.

We can also use these types of systems in the question-answering domain or monitoring domains where the comparison of data can make a strong solution and procedure for completing the tasks. 

Advantages of the Expert System

There are various advantages of using the expert system with a problem domain. Some of the major advantages of the expert system are as follows:

• The availability of the expert system is very high. As we have seen in the examples of the expert system section the systems we talked about are one of the leading expert systems. Instead of them, all their various systems are available which can be used by us.
• Generation of the expert system is very reasonable because the components it requires are easily available and easily can be designed at a low cost and time.
• Since the major work of the system is to learn in real; time they provide highly accurate results which also reduces the fear of failure.
• Because of speed and reliability, these systems reduce the amount of work required.
• These systems require less amount of maintenance and also give the response in a steady way for its whole life cycle.

Final Words

In the article, we have gained knowledge about the expert system which is a major concept in the field of artificial intelligence. As of now, we know how the system works basically and what are points which make a perfect replacement of the human system where the human takes time to solve the problem. Along with these things we have seen some of the real-life examples of the systems with what are the advantages of an expert system.