Also known as Knowledge Management System (KMS); works on the principle of Artificial intelligence. The expert system is designed in such a way that it takes and store the knowledge of a human expert on a particular subject and use that knowledge to assist people with lower expertise to make decisions. Expert system senses your actions on the basis of the actions you have taken in past in similar situations and take decision on the basis of those logical assumptions. They are excellent for diagnostic and prescriptive problems. The goal of the researchers in this area is not to remove people but to make them more productive.
An expert system is programmed with all theories and knowledge in a particular area of expertise such that it is able to make decisions like human experts. It can therefore be looked at as a computer-based system able simulates human thought (process of reasoning) to solve problems that requires human experts.
Characteristic / Features of Expert System
i. The Highest Level of Expertise: The expert system offers the highest level of expertise. It provides efficiency, accuracy and imaginative problem-solving.
ii. 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.
iii. Performs well in problem areas which requires human expert;
iv. It retains flexibility i.e. the expert system is capable of modification and changes as new expertise and information is discovered.
v. Makes it understandable what it knows as the reasons for it answers.
vi. Capable of handling challenging decision & problems: An expert system is capable of handling challenging decision problems and delivering solutions.
Components of expert system
I. Knowledge based subsystem: – This contain facts and rules recorded as data for the system. Rules indicate how to use the facts to solve specific problems.
II. Inference Engine- This provides direction organizes and controls the processing of knowledge and rules to solve problems. It comprises of 3 elements
• Rules interpreter which effectively executes the rules
• Scheduler – it controls when things should be performed and in what order.
• Consistency checker- maintains a consistence form of process and resolution.
III. User interface – This is a component that provides communication between the interference engine and the user.
IV. Knowledge Acquisition program – This enables the expert system to acquire new knowledge and rules.
V. Working Memory – These are temporary working Spaces which contain or store various facts and rules to use during enquiry and adding information to it by user. E.g. the RAM.
VI. Explanation facility- It explains to the user how expert system arrives at its conclusion; it gives a breakdown of the process that was followed.
Examples of expert system: –
1. MYCIN is an expert system for diagnosing and recommending treatment of bacterial infections of the blood (such as meningitis and bacteremia). It is intended to support clinicians in the early diagnosis and treatment of meningitis, which can be fatal if not treated in time.
2. PROSPECTOR is an expert system which was designed for decision making problems in mineral exploration. It uses a structure called an inference network to represent the data base.
3. XCON -The XCON system played a major role in stimulating commercial interest in rule based expert systems. It was designed to replace human “technical editors”. Such editors had been responsible for two operations: checking for completeness of orders, and laying out the physical placement and wiring connections between component modules in the chassis.
4. PXDES: Expert system used to predict the degree and type of lung cancer
5. CaDet: Expert system that could identify cancer at early stages