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