Expert systems are a type of artificial intelligence application that uses knowledge about a specialized domain to provide expert-level advice. An expert system consists of a knowledge base containing facts and rules about the domain, and an inference engine that reasons about the knowledge base like a human expert would. Expert systems were developed to help solve complex problems in domains like medicine, engineering, and business by embodying the knowledge and reasoning of human experts.

1. By
G.VIDYA

2.  One of the most successful applications of artificial
intelligence reasoning techniques using facts and rules
has been in building expert systems that embody
knowledge about a specialized field of human
endeavor such as medicine, engineering or business.
 An expert system has a unique structure, different
from traditional programs. It is divided into two parts,
one fixed, independent of the expert system: the
inference engine, and one variable: the knowledge
base. To run an expert system, the engine reasons
about the knowledge base like a human.

3.  A knowledge based information system that uses its
knowledge about a specific, complex application to act
as an expert consultant to end users.

4.  Expert systems were introduced by researchers in the
Stanford Heuristic Programming Project, including
the "father of expert systems" Edward Feigenbaum,
with the Dendral and Mycin systems. Principal
contributors to the technology were Bruce Buchanan,
Edward Shortliffe, Randall Davis, William vanMelle,
Carli Scott and others at Stanford.

5.  Knowledge base-facts about specific subject area and
heuristics that express the reasoning procedures of an
expert.
 Software resources-inference engine and other
programs refining knowledge and communicating
with users.

6.  Interpretation-inferring situation descriptions from
sensor data.
 Prediction-inferring likely consequences of given
situations.
 Diagnosis-inferring malfunctions from observations.
 Design-configuring objects under constraints.
 Planning-designing actions.
 Control-governing overall system behavior.

7.  Case based-examples of past performance, occurances
and experiences.
 Frame based-network of entities consisting of a
complex package of data values.
 Object based-date and the methods that act on those
data.
 Rule based-rules and statements that typically take the
form of a premise and a conclusion.

8. REASONS FOR GROWTH OF DECISION MAKING:
 People need to analyze large amounts of information.
 People must make decisions quickly.
 People must protect the corporate asset of
organizational information.
 People must apply sophisticated analysis techniques
such as modeling and forecasting to make good
decisions.

9.  Domain : The domain or subject area of the problem is relatively
small and limited to a well – defined problem area.
 Expertise : Solutions to the problem require the efforts of an
expert. That is, a body of knowledge, techniques and intuition is
needed that only a few people possess.
 Complexity : Solution of the problem is a complex task that
requires logical inference processing, which would not be
handled as well by conventional information processing.
 Structure : The solution process must be able to lope with ill –
structured, uncertain, missing and conflicting data and a
problem situation that changes with the passage of time.
 Availability : An expert exists who is articulate and cooperative
and who has the support of the management and users involved
in the development of the proposed system.

10.  Permanence - Expert systems do not forget, but human experts may
 Reproducibility - Many copies of an expert system can be made, but training
new human experts is time-consuming and expensive
 Efficiency - can increase throughput and decrease personnel costs. Although
expert systems are expensive to build and maintain, they are inexpensive to
operate. Development and maintenance costs can be spread over many users.
The overall cost can be quite reasonable when compared to expensive and
scarce human experts.
 Consistency - With expert systems similar transactions handled in the same
way. The system will make comparable recommendations for like situations.
 Documentation - An expert system can provide permanent documentation
of the decision process
 Completeness - An expert system can review all the transactions, a human
expert can only review a sample
 Timeliness - Fraud and/or errors can be prevented. Information is available
sooner for decision making

11.  Common sense - In addition to a great deal of technical
knowledge, human experts have common sense. It is not yet
known how to give expert systems common sense.
 Creativity - Human experts can respond creatively to unusual
situations, expert systems cannot.
 Learning - Human experts automatically adapt to changing
environments; expert systems must be explicitly updated.
 Sensory Experience - Human experts have available to them a
wide range of sensory experience; expert systems are currently
dependent on symbolic input.
 Degradation - Expert systems are not good at recognizing when
no answer exists or when the problem is outside their area of
expertise.

12.  Experts can make fast and good decisions regarding
complex situations.
 Expertise is a task-specific knowledge acquired from
training, reading and experience.
 Expert systems must be constantly updated with new
information.
 Human problem solvers are good only if they operate
in a very narrow domain.
 Expert systems provide limited explanation
capabilities.