INF 308: Expert systems

Course description
The course is about principles, techniques, and tools of developing and managing expert systems, with special emphasis on the practical application of expert systems in Information and Communication Management. Expert knowledge-based problem-solving systems; Theory and application of expert systems: computer systems that capture and use human expertise; The architecture, knowledge and problem- solving style of expert systems; Applications include computer configuration, fault diagnosis, computer-aided instruction, data interpretation, planning and prediction, and process control.

Learning Outcomes:
At the end of the course candidates should be able to: -

  1.   Identify and describe the key characteristics of expert systems
  2.     Demonstrate an understanding of key principles, techniques, and tools of developing and managing expert systems
  3.   Use tools and techniques to develop expert systems.

Course Contents:

  1. Expert knowledge-based problem-solving systems;
  2. Theory and application of expert systems: computer systems that capture and use human expertise;
  3. The architecture, knowledge and problem- solving style of expert systems;
  4. Applications include computer configuration, fault diagnosis, computer-aided instruction, data interpretation, planning and prediction, and process control.

 

EXPERT SYSTEMS

Expert System is a piece of software which uses databases of expert knowledge to offer advice or make decisions in such areas as medical diagnosis.

Examples: There are many examples of expert system. Some of them are given below: MYCIN: One of the earliest expert systems based on backward chaining

MYCIN: One of the earliest expert systems based on backward chaining. It can identify various bacteria that can cause severe infections and can also recommend drugs based on the person’s weight.

DENDRAL: It was an artificial intelligence based expert system used for chemical analysis. It used a substance’s spectrographic data to predict it’s molecular structure.

R1/XCON: It could select specific software to generate a computer system wished by the user.

PXDES: It could easily determine the type and the degree of lung cancer in a patient based on the data.

CaDet: It is a clinical support system that could identify cancer in its early stages in patients.

DXplain: It was also a clinical support system that could suggest a variety of diseases based on the findings of the doctor.

What is an Expert System?

An Expert System is defined as an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. It is a computer application which solves the most complex issues in a specific domain.

 

The expert system can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert. It is also 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:

 

What is an Expert System?

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 examples of Expert Systems

 

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.

DENDRAL: Expert system used for chemical analysis to predict molecular structure.

PXDES: Expert system used to predict the degree and type of lung cancer

CaDet: Expert system that could identify cancer at early stages

Characteristic of Expert System

 

Why Expert Systems are required?

Following are Important characteristic of Expert System:

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

Right on Time Reaction: An Expert System 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 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 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 consists of the following given components:

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. 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 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.

Knowledge Extraction Process

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

Expert Systems can work 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 where expert systems user:

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 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 cannot give creative solutions and can be costly to maintain.

An Expert System can be used in broad applications like Stock Market, Warehouse, HR, etc


https://www.cs.ru.nl/P.Lucas/

https://www.scribd.com/doc/52821660/Expert-Systems-Characteristics