Expert systems: certainty factor

That is why systems that can handle this kind of uncertainty are mandatory elements of an expert system. Uncertainty in expert systems can be handled in a variety of approaches certainty factors, fuzzy logic, and Bayesian theroy. 

Even the concept of certainty factors is not derived from a formal mathematical basis it is the most commonly used method to describe uncertainty in expert systems. This is mainly due to the fact that certainty factors are easy to compute and can be used to effectively reduce search by eliminating branches with low certainty. However, it is difficult to produce a consistent and accurate set of certainty factors. In addition, they are not consistently reliable a certainty factor may produce results opposite to probability theory. 

Whereas expert system languages for reasoning like PROLOG are universally qualified (all/all not) and nonquantitative, MYCIN allows an aspect of existential qualification (some, some not) and is quantified. The rules have both a rule form as above, and a certainty factor (CF). For those of a mathematical bent, the MYCIN certainty factor is defined by the following equation ... 

The reliability of data communication elements is an important constituent part on the quality of transferred information (IQ). It may be stated that IQ consists of accessibility, actual value, completeness, credibility, flexibility, form, meaning over time, relevance, reliability, selectivity, validity (Olaisen 1990, MITIQ). All the above components of IQ tend to be encumbered by some uncertainty. Whether associated with measurement or caused by the unreliability of equipment or software. Hence, the proposal to determine IQ using uncertainty modelling. There are several major methods of uncertainty modelling. The best known are Bayes networks based on conditional probability, the theory mathematical evidence based on the Dempster-Shafer theory, the method making use of the Certainty Factor (Clf) created by Buchanan, Shortliffe for the MYCIN expert system, the theory of fuzzy sets or the theory of rough sets. 

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