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  1. Expert System Architecture:

    An expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise. An expert system’s knowledge is obtained from expert sources and coded in a form suitable for the system to use in its inference or reasoning processes. The expert knowledge must be obtained from specialists or other sources of expertise, such as texts, journal, articles and databases. This type of knowledge usually requires much training and experience in some specialized field such as medicine, geology, system configuration, or engineering design. Once a sufficient body of expert knowledge has been acquired, it must be encoded in some form, loaded into a knowledge base, then tested, and refined continually throughout the life of the system.

    Characteristics Features of Expert Systems:

    Expert systems differ from conventional computer system in several important ways
    1. Expert systems use knowledge rather than data to control the solution process. Much of the knowledge used in heuristic in nature rather than algorithmic.
    2. The knowledge is encoded and maintained as an entity separate from the control program. As such, it is not complicated together with the control program itself. This permits the incremental addition and modification of the knowledge base without recompilation of the control programs. Furthermore, it is possible in some cases to use different knowledge bases with the same control programs to produce different types of expert systems. Such systems are known as expert system shells since they may be loaded with different knowledge bases.
    3. Expert systems are capable of explaining how a particular conclusion was reached, and why requested information is needed during a consultation. This is important as it gives the user a chance to assess and understand the systems reasoning ability, thereby improving the user’s confidence in the system.
    4. Expert systems use symbolic representations for knowledge and perform their inference through symbolic computations that closely resemble manipulations of natural language.
    5. Expert systems often reason with meta-knowledge, that is, they reason with knowledge about themselves, and their own knowledge limits and capabilities.

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