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By

G.VIDYA

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One of the most successful applications of artificialintelligence reasoning techniques using facts and ruleshas been in building expert systems that embodyknowledge about a specialized field of humanendeavor such as medicine, engineering or business.

An expert system has a unique structure, differentfrom traditional programs. It is divided into two parts,one fixed, independent of the expert system: theinference engine, and one variable: the knowledgebase. To run an expert system, the engine reasonsabout the knowledge base like a human.

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A knowledge based information system that uses its

knowledge about a specific, complex application to actas an expert consultant to end users.

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Expert systems were introduced by researchers in theStanford Heuristic Programming Project, including

the "father of expert systems" Edward Feigenbaum,with the Dendral and Mycin systems. Principalcontributors to the technology were Bruce Buchanan,Edward Shortliffe, Randall Davis, William vanMelle,Carli Scott and others at Stanford.

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Knowledge base-facts about specific subject area andheuristics that express the reasoning procedures of an

expert. Software resources-inference engine and other

programs refining knowledge and communicatingwith users.

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Interpretation-inferring situation descriptions fromsensor 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.

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Case based-examples of past performance, occurancesand 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.

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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 oforganizational information.

People must apply sophisticated analysis techniquessuch as modeling and forecasting to make good

decisions.

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Domain : The domain or subject area of the problem is relativelysmall and limited to a well defined problem area.

Expertise : Solutions to the problem require the efforts of anexpert. That is, a body of knowledge, techniques and intuition isneeded that only a few people possess.

Complexity : Solution of the problem is a complex task thatrequires logical inference processing, which would not behandled as well by conventional information processing.

Structure : The solution process must be able to lope with ill structured, uncertain, missing and conflicting data and aproblem situation that changes with the passage of time.

Availability : An expert exists who is articulate and cooperativeand who has the support of the management and users involvedin the development of the proposed system.

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Permanence - Expert systems do not forget, but human experts may

Reproducibility - Many copies of an expert system can be made, but trainingnew human experts is time-consuming and expensive

Efficiency - can increase throughput and decrease personnel costs. Althoughexpert 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 andscarce human experts.

Consistency - With expert systems similar transactions handled in the sameway. The system will make comparable recommendations for like situations.

Documentation - An expert system can provide permanent documentationof the decision process

Completeness - An expert system can review all the transactions, a humanexpert can only review a sample

Timeliness - Fraud and/or errors can be prevented. Information is availablesooner for decision making

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Common sense - In addition to a great deal of technical

knowledge, human experts have common sense. It is not yetknown 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 awide range of sensory experience; expert systems are currentlydependent on symbolic input.

Degradation - Expert systems are not good at recognizing whenno answer exists or when the problem is outside their area ofexpertise.

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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.

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