The field of Fuzzy Control have enjoyed tremendous success in the last decade, with both theoretical and industrial developments being introduced at an increasing rate I. However, fuzzy control is just one application of Soft Computing methods in general, and Fuzzy Sets theory in particular. This principled approach to approximate reasoning is not limited only to control problems, but is useful also in closely related fields, such as Artificial Intelligence (AI) in its various forms and guises, Decision Sciences, Quantitative and Qualitative Decision Theory, theoretical studies of Uncertainty, Information and Knowledge, etc. As we seek to expand the success of fuzzy control to these fields, it is very tempting to attempt use of known methods in a familiar way to solve problems in these new domains. However, a principled analysis of the goals and assumptions underlying different fields may reveal important differences from the field of control which may make current methods insufficient.