Expert System in AI: What is, Applications & Example

The Expert System in AI can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert. Artificial Intelligence and Expert Systems are capable of expressing and reasoning about some domain of knowledge. Expert systems were the predecessor of the current day artificial intelligence, deep learning and machine learning systems.

In this tutorial, you will learn:

• Examples of Expert Systems
• Characteristic of Expert System
• Components of the Expert System
• Other Key terms used in Expert Systems
• Participant in Expert Systems Development
• Conventional System vs. Expert System
• Human expert vs. Expert System
• Benefits of Expert Systems
• Limitations of the Expert System
• Applications of Expert Systems

Examples of Expert Systems

Following are the Expert System Examples:

• MYCIN: It was based on backward chaining and could identify various bacteria that could cause acute infections. It could also recommend drugs based on the patient’s weight. It is one of the best Expert System Example.
• DENDRAL: Expert system used for chemical analysis to predict molecular structure.
• PXDES: An Example of Expert System used to predict the degree and type of lung cancer
• CaDet: One of the best Expert System Example that can identify cancer at early stages

Characteristics of Expert System

Following are the important Characteristics of Expert System in AI:

• The Highest Level of Expertise: The Expert system in AI offers the highest level of expertise. It provides efficiency, accuracy and imaginative problem-solving.

• Right on Time Reaction: An Expert System in Artificial Intelligence interacts in a very reasonable period of time with the user. The total time must be less than the time taken by an expert to get the most accurate solution for the same problem.

• Good Reliability: The Expert system in AI needs to be reliable, and it must not make any a mistake.

• Flexible: It is vital that it remains flexible as it the is possessed by an Expert system.

• Effective Mechanism: Expert System in Artificial Intelligence must have an efficient mechanism to administer the compilation of the existing knowledge in it.

• Capable of handling challenging decision & problems: An expert system is capable of handling challenging decision problems and delivering solutions.

Components of the Expert System

The Expert System in AI consists of the following given components:

User Interface

The user interface is the most crucial part of the Expert System Software. 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. Inference engine contains rules to solve a specific problem. It refers the knowledge from the Knowledge Base. 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 Software mainly depends on the highly accurate and precise knowledge.

Other Key terms used in Expert Systems

Facts and Rules

A fact is a small portion of important information. Facts on their own are of very limited use. The rules are essential to select and apply facts to a user problem.

Knowledge Acquisition

The term knowledge acquisition means how to get required domain knowledge by the expert system. The entire process starts by extracting knowledge from a human expert, converting the acquired knowledge into rules and injecting the developed rules into the knowledge base.

Participant in Expert Systems Development

Participant	Role
Domain Expert	He is a person or group whose expertise and knowledge is taken to develop an expert system.
Knowledge Engineer	Knowledge engineer is a technical person who integrates knowledge into computer systems.
End User	It is a person or group of people who are using the expert system to get to get advice which will not be provided by the expert.

The process of Building An Expert Systems

• Determining the characteristics of the problem
• Knowledge engineer and domain expert work in coherence to define the problem
• The knowledge engineer translates the knowledge into a computer-understandable language. He designs an inference engine, a reasoning structure, which can use knowledge when needed.
• Knowledge Expert also determines how to integrate the use of uncertain knowledge in the reasoning process and what type of explanation would be useful.

Conventional System vs. Expert System

Conventional System	Expert System
Knowledge and processing are combined in one unit.	Knowledge database and the processing mechanism are two separate components.
The programme does not make errors (Unless error in programming).	The Expert System may make a mistake.
The system is operational only when fully developed.	The expert system is optimized on an ongoing basis and can be launched with a small number of rules.
Step by step execution according to fixed algorithms is required.	Execution is done logically & heuristically.
It needs full information.	It can be functional with sufficient or insufficient information.

Human expert vs. Expert System

Human Expert	Artificial Expertise
Perishable	Permanent
Difficult to Transfer	Transferable
Difficult to Document	Easy to Document
Unpredictable	Consistent
Expensive	Cost effective System

Benefits of Expert Systems

• It improves the decision quality
• Cuts the expense of consulting experts for problem-solving
• It provides fast and efficient solutions to problems in a narrow area of specialization.
• It can gather scarce expertise and used it efficiently.
• Offers consistent answer for the repetitive problem
• Maintains a significant level of information
• Helps you to get fast and accurate answers
• A proper explanation of decision making
• Ability to solve complex and challenging issues
• Artificial Intelligence Expert Systems can steadily work without getting emotional, tensed or fatigued.

Limitations of the Expert System

• Unable to make a creative response in an extraordinary situation
• Errors in the knowledge base can lead to wrong decision
• The maintenance cost of an expert system is too expensive
• Each problem is different therefore the solution from a human expert can also be different and more creative

Applications of Expert Systems

Some popular Application of Expert System:

• Information management
• Hospitals and medical facilities
• Help desks management
• Employee performance evaluation
• Loan analysis
• Virus detection
• Useful for repair and maintenance projects
• Warehouse optimization
• Planning and scheduling
• The configuration of manufactured objects
• Financial decision making Knowledge publishing
• Process monitoring and control
• Supervise the operation of the plant and controller
• Stock market trading
• Airline scheduling & cargo schedules

Summary

• An Expert System is an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problem
• Key components of an Expert System are 1) User Interface, 2) Inference Engine, 3) Knowledge Base
• Key participants in Artificial Intelligence Expert Systems Development are 1) Domain Expert 2) Knowledge Engineer 3) End User
• Improved decision quality, reduce cost, consistency, reliability, speed are key benefits of an Expert System
• An Expert system can not give creative solutions and can be costly to maintain.
• An Expert System can be used for broad applications like Stock Market, Warehouse, HR, etc