What is Expert System in Artificial Intelligence – How it Solve Problems

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In this Expert System Tutorial, we will learn what is Expert System in Artificial Intelligence. Also, will learn the components, characteristics and types of AI Expert System.

Along with this, will study Expert System benefits and disadvantages of AI. Besides, all will use images to represent it in a better way and for better understanding.

So, let’s start exploring Expert System in Artificial Intelligence.

What is the Expert System in Artificial Intelligence?

We can say, the Expert System in AI are computer applications. Also, with the help of this development, we can solve complex problems. It has level of human intelligence and expertise.

a. Characteristics of Expert System in AI

  • High performance
  • Reliable
  • Highly responsive
  • Understandable

b. Capabilities of Expert System in AI

The expert systems are capable of −
  • Advising
  • Instructing and assisting a human in decision making
  • Demonstrating
  • Deriving a solution
  • Diagnosing
  • Explaining
  • Interpreting input
  • Predicting results
  • Justifying the conclusion
Suggesting alternative options to a problem. They are incapable of −
  • Substituting human decision makers
  • Possessing human capabilities
  • Producing accurate output for inadequate knowledge base
  • Refining their own knowledge

Components of Expert System in Artificial Intelligence

The components of AI expert system include −
  • Knowledge Base
  • Inference Engine
  • User Interface
Let us see them one by one briefly
Components of Expert System in Artificial Intelligence

Components of Expert System in Artificial Intelligence

Knowledge-Based in Expert System

Generally, it contains domain-specific and high-quality knowledge. Also, as to exhibit intelligence required Knowledge. Although, a collection of highly accurate and precise knowledge is the reason for the success of Expert System in Artificial Intelligence.

a. What is Knowledge?

Basically, data is a collection of facts. Also, have to organize the information as data and facts about the task domain. Further, we can define knowledge as the combinations of Data, information, and past experience.

b. Components of Knowledge Base

Basically, there are two components present:
  • Factual Knowledge – Generally, knowledge Engineers and Scholar used this in the task domain.
  • Heuristic Knowledge – Generally, we can say it’s all about practice, accurate judgment and one’s ability of evaluation.

c. Knowledge Representation

Basically, it’s types of method. We use this to organize and formalize knowledge in a knowledge base. Although, this method is in the form of IF-THEN-ELSE rules.

d. Knowledge Acquisition

Basically, quality and accuracy are the key reasons for the success of an expert system in AI.
Also, knowledge engineer acquires exact information. Although, as they collect this information from a subject expert. He has had various ways to collect the information in different ways. Such as by recording, interviewing, and observing him at work, etc.
He uses IF-THEN-ELSE rules, to organize and categorize information in a meaningful way. The knowledge engineer also monitors the development of the Expert System.

Inference Engine

To arrive a particular solution, Inference Engine acquires and manipulates the knowledge.
In case of rule-based Expert System in Artificial Intelligence −
  • We have to apply rules to the facts. That is obtained from earlier rule application.
  • Also, we have to add new knowledge if it’s required.
  • Basically, it can resolve rules conflict. Whenever multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
  • Forward Chaining
  • Backwards Chaining

a. Forward Chaining

We can say that it’s a type of strategy of an expert system. In this, we have to answers this question, “What can happen next?”
Generally, it follows the chain of conditions and derivations. Also, reduces the outcome. Although, it needs to consider all the facts and rules. Further, have to sort them before concluding to a solution. Moreover, this is followed by working on a result.
For example:
Prediction of share market status as an effect of changes in interest rates.
Knowledge Base in Expert Systems

Forward Chaining

b. Backward Chaining

As with the use of this strategy, an expert system finds out the answer to the question, “Why this happened?”
Basically, as what has already happened matter a lot. Thus, it tries to find out which conditions could have happened in the past for this result. Hence, this strategy is followed by finding out cause or reason.
For example:
Diagnosis of blood cancer in humans.
Knowledge Base in Expert Systems

Knowledge Base in Expert Systems – Backward Chaining

User Interface

Generally, Expert System in AI users and ES itself uses User interface as a medium of interaction between users. Also, the user of the Expert Systems need not be necessarily an expert in Artificial Intelligence.
Although, at a particular recommendation, it explains how the Expert Systm has arrived. Hence, the explanation may appear in the following forms −
  • Basically, the natural language displayed on a screen.
  • Also, verbal narrations in natural language.
Further, listing of rule numbers displayed on the screen. The user interface makes it easy to trace the credibility of the deductions.

Disadvantages of Expert Systems in AI

Basically, we have noticed that no technology can offer an easy and complete solution. Also, large systems are too costly. Although, they require significant development time and computer resources.
Also, AI Expert Systems have their limitations which include −
  • Limitations of the technology
  • Difficult knowledge acquisition
  • Expert Systems are difficult to maintain
  • High development costs

Expert System Technology

Technologies used in Expert Systems in Artificial Intelligence includes:

a. Expert Systems Development Environment

Basically, hardware and tools are included in it. They are −
  • Minicomputers, workstations, mainframes.
  • LISt Programming (LISP) and PROgrammation en LOGique (PROLOG).
  • Large databases.

b. Tools

Generally, tools are used to reduce the effort and cost.
  • Powerful editors and debugging tools with multi-windows.
  • They provide rapid prototyping.
  • Have Inbuilt definitions of a model, knowledge representation, and inference design.

Benefits of Expert Systems in Artificial Intelligence

a. Availability
Due to mass production of software, expert systems are easily available.
b. Less Production Cost
As the production cost of an expert system is reasonable. Thus, it makes them affordable.
c. Speed
Expert systems offer great speed. Also, reduce the amount of work that an individual puts in.
d. Less Error Rate
Generally, an error rate of the expert system is low in comparison to human errors.
e. Reduced danger
They can be used in any risky environments where humans cannot work with.
f. Permanence
The knowledge will last long indefinitely.
g. Multiple expertise
It can be designed to have knowledge of many experts.
h. Explanation
They are capable of explaining in detail the reasoning that led to a conclusion.

AI Expert System with Applications

a. Design Domain
We use expert systems in designing of camera lens and automobile.
b. Monitoring Systems
In this data is compared with an observed system
c. Process Control Systems
We have to control physical process based on monitoring
d. Knowledge Domain
Finding out faults in vehicles, computers
e. Finance Commerce
Expert system is used to detect possible fraud.
So, this was all about Expert System in Artificial Intelligence. Hope you like our explanation.


As a result, we have studied Expert Systems in artificial intelligence. Also, learned characteristics, components, types and benefits along with expert systems applications of AI.

As we have images that will help you in better understanding. Furthermore, if you feel any query, feel free to ask in a comment section.

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