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Expert System in Artificial Intelligence : Role, History & Types

expert system in artificial intelligence
expert system

What is an Expert System?

An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries.

First Expert System:  

The expert system is a part of AI, and the first ES was developed in the year 1970, which was the first successful approach of artificial intelligence. It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base. The system helps in decision making for complex problems using both facts and heuristics like a human expert. It is called so because it contains the expert knowledge of a specific domain and can solve any complex problem of that particular domain. These systems are designed for a specific domain, such as medicine, science, etc.

The performance of an expert system is based on the expert’s knowledge stored in its knowledge base. The more knowledge stored in the KB, the more that system improves its performance. One of the common examples of an ES is a suggestion of spelling errors while typing in the Google search box.

History of Expert System

Early development

Soon after the dawn of modern computers in the late 1940s – early 1950s, researchers started realizing the immense potential these machines had for modern society. One of the first challenges was to make such a machine capable of “thinking” like humans. In particular, making these machines capable of making important decisions the way humans do. The medical / healthcare field presented the tantalizing challenge to enable these machines to make medical diagnostic decisions.

Thus, in the late 1950s, right after the information age had fully arrived, researchers started experimenting with the prospect of using computer technology to emulate human decision-making.

Formal introduction & later developments

This previous situation gradually led to the development of expert systems, which used knowledge-based approaches. These expert systems in medicine were the MYCIN expert system, the INTERNIST-I expert system and later, in the middle of the 1980s, the CADUCEUS.

Current approaches to expert systems

The limitations of the previous type of expert systems have urged researchers to develop new types of approaches. They have developed more efficient, flexible, and powerful approaches in order to emulate the human decision-making process. Some of the approaches that researchers have developed are based on new methods of artificial intelligence (AI), and in particular in machine learning and data mining approaches with a feedback mechanism. Related is the discussion on the disadvantages section.

Structure of ES: 

  • The internal structure of an expert system can be considered to consist of three parts:
  • The knowledgebase; the database; the rule interpreter.
  • This is analogous to the production system where we have
  • The set of productions; the set of facts held as working memory and a rule interpreter.
  • The knowledge base holds the set of rules of inference that are used in reasoning. Most of these systems use IF-THEN rules to represent knowledge. Typically systems can have from a few hundred to a few thousand rules.
  • The database gives the context of the problem domain and is generally considered to be a set of useful facts. These are the facts that satisfy the condition part of the condition action rules as the IF-THEN rules can be thought of.
  • The rule interpreter is often known as an inference engine and controls the knowledge base using the set of facts to produce even more facts. Communication with the system is ideally provided by a natural language interface. This enables a user to interact independently of the expert with the intelligent system.

Types of Expert System Technology

ES technologies come at various levels, they are:

Expert System Development Environment

The ES development environment contains a set of hardware tools (Workstations, minicomputers, mainframes), High-level symbolic programming languages [List Programming (LISP), and PROgrammation en LOGique (PROLOG)], as well as large databases.


Tools, as an ES technology, assists in reducing the effort and cost involved in developing an expert system to a large extent.


A Shell an expert system that functions without a knowledge base. It provides developers with knowledge acquisition, inference engine, user interface, and explanation facility. For example – Java Expert System Shell (JESS), Vidwan, etc.


User Interface

The user interface is the most crucial part of the expert system. This component takes the user’s query in a readable form and passes it to the inference engine. After that, it displays the results to the user. In other words, it’s an interface that helps the user communicate with the expert system.

Inference Engine

The inference engine is the brain of the expert system. The inference engine contains rules to solve a specific problem. And It refers to the knowledge from the Knowledge Base. Also It selects facts and rules to apply when trying to answer the user’s query. It provides reasoning about the information in the knowledge base. It also helps in deducting the problem to find the solution. This component is also helpful for formulating conclusions.

Knowledge Base

The knowledge base is a repository of facts. It stores all the knowledge about the problem domain. It is like a large container of knowledge which is obtained from different experts of a specific field.

Thus we can say that the success of the Expert System mainly depends on the highly accurate and precise knowledge.

How Expert Systems Work

This page explains how expert systems work and the best way to do that is to take a case study which is MYCIN. There are a few specific steps that can be followed in order to build up an expert system

 E.g. MYCIN.

  • First, the expert system must be fed its ‘knowledge’. Human experts, in this case, doctors specializing in bacterial infections, contribute their information on a particular subject, which is programmed into the system. The MYCIN system contains information on about 100 causes of bacterial infection. 
  • Next, information on a new problem is presented to the system. A doctor trying to determine the presence and cause of bacterial infections may input the patient’s symptoms, general condition, and medical history, as well as results from simple lab tests.

You may also know: A complete overview of cloud computing

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