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Expert System in Artificial Intelligence

Expert System in Artificial Intelligence

  • 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.
  • It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base.

  •  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.

 Example of expert system :-

  • DENDRAL: It was an artificial intelligence project that was made as a chemical analysis expert system. It was used in organic chemistry to detect unknown organic molecules with the help of their mass spectra and knowledge base of chemistry.
  • MYCIN: It was one of the earliest backward chaining expert systems that was designed to find the bacteria causing infections like bacteraemia. It was also used for the recommendation of antibiotics and the diagnosis of blood clotting diseases.

Characteristics of Expert System

  • High Performance: The expert system provides high performance for solving any type of complex problem of a specific domain with high efficiency and accuracy.
  • Understandable: It responds in a way that can be easily understandable by the user. It can take input in human language and provides the output in the same way.
  • Reliable: It is much reliable for generating an efficient and accurate output.
  • Highly responsive: ES provides the result for any complex query within a very short period of time.

#Architecture of Expert System:-


  • knowledge base :- The knowledge base is a type of storage that stores knowledge acquired from the different experts of the particular domain. It is considered as big storage of knowledge. The more the knowledge base, the more precise will be the Expert System. It is similar to a database that contains information and rules of a particular domain or subject. The knowledge base contains the specific domain knowledge that is used by an expert to derive conclusions from facts.
  • Fact database :-The fact database contains the case-specific data that are to be used in a particular case to derive a conclusion. In the case of a medical expert system, this would contain information that had been obtained about the patient’s condition. The user of the expert system interfaces with it through a user interface, which provides access to the inference engine, the explanation system, and the knowledge-base editor.
  • Explanation system :-The explanation system provides information to the user about how the inference engine arrived at its conclusions. This can often be essential, particularly if the advice being given is of a critical nature, such as with a medical diagnosis system. If the system has used faulty reasoning to arrive at its conclusions, then the user may be able to see this by examining the data given by the explanation system.
  • knowledge-base editor :-The knowledge-base editor allows the user to edit the information that is contained in the knowledge base. The knowledge-base editor is not usually made available to the end user of the system but is used by the knowledge engineer or the expert to provide and update the knowledge that is contained within the system.
  • Inference engine:- The inference engine is known as the brain of the expert system as it is the main processing unit of the system. The inference engine is the part of the system that uses the rules and facts to derive conclusions. The inference engine will use forward chaining, backward chaining, or a combination of the two to make inferences from the data that are available to it. There are two types of inference engine:
  • User Interface:-With the help of a user interface, the expert system interacts with the user, takes queries as an input in a readable format, and passes it to the inference engine. After getting the response from the inference engine, it displays the output to the user. In other words, it is an interface that helps a non-expert user to communicate with the expert system to find a solution.

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