It could also recommend drugs based on the patient's weight.

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. 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.

Expert systems

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.

(PDF) Tools of Development of Expert Systems: A comparative study | haider allamy - phon-er.com

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 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. The ways that experts do things involves the manner in which they bring together knowledge and their way of thinking, and this in its turn is nothing other than the way human beings think.

The manner in which experts think can be said to be thinking that involves collection and management of knowledge for the solving of specific problems but can be seen as a miniature of human thinking and humans in general. There are few examples of fuzzy expert systems outside of control, but there are reports of fault analysis systems for locomotives and restaurant menu selection systems.

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An International Journal

Chapter contents Book contents. Applied Fuzzy Systems. Applied Fuzzy Systems , Pages Author links open overlay panel.