An expert system is a computer program that is designed to emulate and mimic human intelligence, skills or behavior. An expert system is an advanced computer application that is implemented for the purpose of providing solutions to complex problems, or to clarify uncertainties through the use of non-algorithmic programs where normally human expertise will be needed. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. Expert systems are most common in the complex problem domain and are considered as widely used alternatives in searching for solutions that require the existence of specific human expertise. So, what is the question; What does mean Expert System? with Advantages and Disadvantages.
Expert systems are most common in the complex problem domain and are considered as widely used alternatives in searching for solutions that require the existence of specific human expertise. The expert system is also able to justify its provided solutions based on the knowledge and data from past users. Normally expert systems are used in making business marketing strategic decisions, analyzing the performance of real-time systems, configuring computers and perform many other functions which normally would require the existence of human expertise.
The difference between an expert system with a normal problem-solving system is that the latter is a system where both programs and data structures are encoded, while for an expert system only the data structures are hard-coded and no problem-specific information is encoded in the program structure. Instead, the knowledge of a human expertise is captured and codified in a process known as knowledge engineering.
Hence, whenever a particular problem requires the assistance of a certain human expertise to provide a solution, the human expertise which has been codified will be used and processed in order to provide a rational and logical solution. This knowledge-based expert system enables the system to be frequently added with new knowledge and adapt accordingly to meet new requirements from the ever-changing and unpredictable environment.
An expert system has been reliably used in the business world to gain tactical advantages and forecast the market’s condition. In this globalization era where every decision made in the business world is critical for success, the assistance provided from an expert system is undoubtedly essential and highly reliable for an organization to succeed.
Examples given below will be the advantages for the implementation of an expert system in business:
Although the expert system does provide many significant advantages, it does have its drawbacks as well.
Examples given below will be the disadvantages for the implementation of an expert system in business:
A good expert system is expected to grow as it learns from user feedback. Feedback is incorporated into the knowledge base as appropriate to make the expert system smarter. The dynamism of the application environment for expert systems is based on the individual dynamism of the components. This can be classified as follows:
Most dynamic: Working memory. The contents of the working memory, sometimes called the data structure, changes with each problem situation. Consequently, it is the most dynamic-component of an expert system, assuming, of course, that it is kept current.
Knowledgebase: The knowledge base need not change unless a new piece of information arises that indicates a change in the problem-solving procedure. Changes in the knowledge base should be carefully evaluated before being implemented. In effect, changes should not be based on just one consultation experience. For example, a rule that is found to be irrelevant less than one problem situation may turn out to be crucial in solving other problems.
Least dynamic: Inference engine. Because of the strict control and coding structure of an inference engine, changes are made only if absolutely necessary to correct a bug or enhance the inferential process. Commercial inference engines, in particular, change only at the discretion of the developer. Since frequent updates can be disruptive and costly to clients, most commercial software developers try to minimize the frequency of updates.
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