Expert systems: information theory

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. 

Fuzzy logic systems grew out of a desire to quantify rule-based expert systems. Fuzzy set theory had provided us with an effective framework for dealing with fuzzy information and for translating control strategies based on an expert knowledge into an automatic control strategy. 

Expert system tools are now available that can deEne and depict the knowledge of an expert. These are known as frame-based systems, which deEne frames or schemas and instantiate those with information that enable them to inherit information from other frames. These systems are practical replications of schema theory. They enable you define the knowledge of the expert as well as converting that knowledge into a production system of if-then rules. Rather than instantiating the frames with information, instantiate the frames with the addresses of the nodes in the hypeitext. That would, in effect, m the expert s knowledge directly onto the hypertext. It would also be important to assess learner s cognitive stractures to assess the extent to which they reflected the expert s. 

The theory of evidence was first generated by Dempster (1968) and further developed by Shafer (1976). It is often referred to as the Dempster-Shafer theory of evidence or D-S theory. The D-S theory was originally used for information aggregation in expert systems as an approximate reasoning tool (Mantaras, 1990). Subsequently, it has been used in decision making under uncertainty (Yager, 2004). 

Based on the fiamework of the proposed model, initially both qualitative and quantitative data were collected based on experts analysis along with available set of potential suppliers from procurement department. Then, the incomplete information collected from experts will be expressed as grey numbers, and grey system theory-based approach is used to obtain the weights and supplier s ratings for each attribute based on expert s opinions. Finally, uncertain-goal programming-based mathematical model is proposed to find the optimum set of suppliers and optimal purchase quantities. 

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