Dealing with Uncertainty in Expert Systems

The aim of artificial intelligence is to develop tools for representing piece of knowledge and providing inference mechanism for elaborating conclusion of knowledge from stored information. The available knowledge is far from being certain, precise and complete. In Expert systems the word uncertainty is related to the working with inexact data, imprecise information, handling identical situation, reliability of the results etc. An expert system allows the user to assign probabilities, certainty factors, or confidence levels and many more techniques to any or all input data. This feature closely represents how most problems are handled in the real world. An expert system can take all relevant factors into account and make a recommendation based on the best possible solution rather than the only exact solution to handle such problems. This paper describes the various types of uncertainty, its sources and different approaches to handle uncertainty.