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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