Expert Systems

Posted: December 9, 2011 in Expert Systems, IT Management, Uncategorized

An expert system is a knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant to end users. It is a component of Artificial Intelligence.

Expert systems provide answers to questions in a very specific problem area by making human like inferences about knowledge contained in a specialized knowledge base.

Expert systems can provide decision support to end users in the form of advice from an expert consultant in a specific problem area.

Expert Systems Structure:

The components of an expert system include a knowledge base and software modules that perform inferences on the knowledge in the knowledge base and communicate answers to a user’s questions.
The knowledge base of an expert system contains –
Facts about a specific area, Heuristics (thumbs of rule) that express the reasoning procedures of an expert on the subject. There are many ways that knowledge is represented in expert systems:

Case-based reasoning: Representing knowledge in an expert system’s knowledge base in the form of cases.

Frame-based knowledge: Knowledge represented in the form of a hierarchy or network of frames. A frame is a collection of knowledge about an entity consisting of a complex package of data values describing its attributes.

Object-based knowledge: Knowledge represented as a network of objects. An object is a data element that includes both data and the methods or processes that act on those data.

Rule-based knowledge: Knowledge represented in the form of rules and statements of fact. Rules are statements that typically take the form of a premise and a conclusion such as: IF (condition), Then (conclusion).

Software resources: An expert system software package contains an inference engine and other programs for refining knowledge and communicating with users. The inference engine program processes the knowledge (such as rules and facts) related to a specific problem. It then makes associations and inferences resulting in recommended courses of action for a user. User interface programs for communicating with end-users are also needed, including an explanation program to explain the reasoning process to a user if requested.
Differences between DSS and ES:

  • It is possible to integrate ES with DSS. There may be some components which may look similar in DSS and ES. But one should understand the differences between them. It then becomes clear as to how integration of ES with DSS can be realized.
  • A DSS helps manager to take a decision whereas an ES acts as a decision maker or an advisor to the manager.
  • A DSS is meant only for decision making whereas an ES provides expertise to the manager.
  • The spectrum of complexity is high in DSS and low in ES since ES addresses issues related to specific areas only.
  • DSS does not capability to reason whereas an ES has.
  • A DSS cannot provide detailed explanation about the results whereas an ES can.

Hence by integrating the two it is possible the blend their advantages and derive the best out of the two.

Expert systems help diagnose illness, search minerals, analyze compounds, recommend repairs, and do financial planning. So from a strategic business point, expert systems can and are being used to improve every step of the product cycle of a business, from finding customers to shipping products to providing customer service. ES provides a cost reduced solution, consistent advice with low level of errors, solution to handle equipments without the interference of human. It provides a high degree of reliability and faster response time. It helps to solve complex problem with in a small domain.

It is capable of analyzing the problem and can construct a business model appropriate to the characteristics of the application.Based on the model necessary objectives and constraints are identified. It identifies appropriate tools to solve the model. It uses the tools to solve the problem and also does the what – if analysis aimed at understanding the sensitivity of the model.


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