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Expert System in Artificial Intelligence

Expert systems have emerged as the ideal alternatives to solutions for complex problems that necessitate a uniquely human skill. 


An expert system in artificial intelligence is a system that leverages computers to make decisions for challenging and intricate issues. 


If you enroll in an AI & ML course, you’ll learn that an expert system is the first practical application of AI. Such courses will also tell how to leverage logical thought and knowledge and adhere to certain guidelines to resolve issues. 


This article will give you a complete overview of what an expert system is in artificial intelligence. So, let’s start. 


What is an Expert System?

An expert system in artificial intelligence is computerized software created to solve complicated issues and offer decision-making capabilities similar to those of a human expert. 


This is accomplished by leveraging inference and reasoning rules to retrieve information from the system’s knowledge base in accordance with user queries.


How Does an Expert System Work?

Today’s cutting-edge expert system models leverage artificial intelligence and machine learning to replicate the judgment or behavior of domain experts. 


With more experience over time, an expert system will also enhance their performance like humans. The best expert system will gather facts and experiences in the knowledge base and combine them with rules or an inference engine. 


This inference engine leverages either the forward chaining or backward chaining method (learn more about these methods in the later section) to collect and gather information from the knowledge base. 


This overall procedure of developing and maintaining an expert system is referred to as knowledge engineering. Many individuals now seek interest in the role of knowledge engineers as the part of the job is quite enticing. 


You’ll have to make sure the expert systems in AI consist of every required information to resolve an issue. If you’re about to give an interview for the role of data or knowledge engineer, prepare better with these common data engineering interview questions


What are the Components of an Expert System in AI?

  1. Knowledge Base

    The knowledge base consists of facts and regulations. It includes expertise in a certain field, norms for resolving issues, steps to take, and intrinsic facts pertinent to the field.

  2. Inference Engine

    The purpose of the inference engine in an expert system is to retrieve pertinent information from the knowledge base, analyze it, and identify a solution that addresses the problem being encountered by the user. 

  3. Forward Chaining

    The Inference Engine, in this situation, follows a series of prerequisites and inferences to arrive at a conclusion. Before coming to a conclusion, it weighs all the information and organizes the rules.

  4. Backward Chaining

    The Inference Engine determines which past circumstances may have led to this outcome based on what has already occurred. This expert system component is used to determine the cause or reason. 

  5. Knowledge Acquisition and Learning Module

    This element allows expert systems to gather more data from a variety of sources. The information is then kept secure in the knowledge base.

  6. User Interface

    The expert system communicates with the user through an intuitive user interface, receives queries in a legible format, and sends those queries to the inference engine. 

  7. Explanation Module

    This module assists the expert system in explaining to the user how it arrived at a specific conclusion.


What are the Characteristics of an Expert System in AI?

Here is a list of the major characteristics of an expert system in AI: 


  • Top-Notch Expertise: All types of the expert system ensure top-notch expertise in addition to seamless accuracy and efficiency. 
  • Reaction Time: The reaction time of an expert system is very low. When compared to a human expert, resolving the same challenging issue consumes less time. 
  • Reliable & Flexible: An expert system is not only highly reliable due to its error-free functioning but also flexible in dealing with unique problems. 
  • Efficient & Capable: An expert system leverages a substantial mechanism to deal with complicated problems, administer them on time, and deliver effective solutions. 


Advantages of Expert Systems

There are several advantages of using an expert system in AI over a human expert: 


  • Precision: Expert systems are impervious to subjective or human error. They make decisions based on information and laws.
  • Permanent: Human professionals may leave technical information behind after departing their posts. However, an expert system provides an endless supply of knowledge and information.
  • Logical Conclusion: An expert system uses a range of concepts, like the if-then rules, to draw conclusions from existing facts through the process of logical reasoning.
  • Cost-Effective: When compared to the cost of hiring a human expert, expert systems are inexpensive. They can help people reach decisions more swiftly and affordably.


What are Expert Systems Limitations?

Here is a list of all the limitations of using an expert system: 


  • In the event that the knowledge base comprises wrong information, your expert system response will also get wrong. 
  • Like human experts, an expert system cannot generate a creative output for every scenario. 
  • The cost of development and maintenance via an expert system is high.
  • One of the major limitations of an expert system is that you’ll need a specific ES for every domain. 
  • The fact that an expert system cannot learn by itself means you’ll have to update it manually. 


Examples of Expert System in AI

  • DENDRAL: An expert system leveraged to predict molecular structure via chemical analysis. 
  • CaDet: It is a unique expert system example that can detect cancer at its early or preliminary stages. 
  • PXDES: This expert system is used to predict the type and stage of lung cancer.
  • MYCIN: It follows the backward chaining methods to detect multiple bacteria that can lead to acute infections. 


What are the Applications of Expert Systems?

Here are a few applications of expert systems in AI in today’s AI-powered world: 


  • Manufacturing & Designing Domain: It has a wide range of applications in designing and manufacturing tangible objects, including producing and designing camera lenses and automobiles. 
  • Finance Domain: It finds applications in the financial sector to identify potential frauds and suspicious conduct, as well as to advise bankers on whether or not to offer business loans.
  • Knowledge Domain: These systems are typically employed in the domain to disseminate information to users. Tax advisors and advisors are the two common ES leveraged for this subject.



Conclusively, an expert system is reliable, interactive, and helpful in resolving complex problems. From medicine to human resources, an expert system serves various purposes. 


Whether it’s cost savings, reliability, enhanced decision quality, rapidity, or consistency, you’ll find all these benefits within an expert system in AI. As AI data structures and algorithms gain traction every day, having a complete overview of data structures and algorithms has become crucial. 


So, if you want to excel in the field of ML and AI, then enroll in a course, learn about the types of data structures in detail, and become a knowledge or data engineer in no time! 




Some important characteristics of an expert system in AI include a high level of accuracy, simple-to-understand methodologies, trustworthiness, and good receptiveness.
To build an expert system in AI, follow these steps: 
  • Identify and determine the issue’s characteristics.
  • Knowledge engineers must decipher or define the problem by collaborating with domain experts.
  • Once the problem is addressed, the knowledge engineer must translate it into comprehensible computer language knowledge.
From the stock market and healthcare to human resources, the application of expert systems can be found everywhere. It is important for these domains as it helps them with reliability, speed, consistency, cost reduction, and improved decision quality.
CaDet, also known as Cancer Decision Support Tool, is an example of an expert system that helps detect cancer in its preliminary stage.

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