A general framework is proposed for the development of uncertainty measures in finite and infinite domains in the context of evidence theory. The proposed measures account uncertainty in the form of information ratios, which relate an uncertainty situation to the maximum uncertainty-based information present in its domain. They are shown to be ideal for the measurement of uncertainty of interval based structures such as evidence sets. Uncertainty, Uncertainty in nondiscrete Domains, Uncertain Reasoning, Evidence Theory. Introduction To develop an extended theory of approximate reasoning whose mathematical structures can capture all forms of uncertainty recognized in generalized information theory, we need set structures with membership functions that more than fuzzy are also nonspecific and conflictive.
Evidence sets capture fuzziness, nonspecificity, and conflict in their membership degrees.
It is defended that a general framework for measuring uncertainty should be built around the notion of relative uncertainty , that is, when the uncertainty content of some situation is related to the information content of the universal set on which the situation is defined.