Abstract
A personal account of the emergence and development of generalized information theory (GIT) in the context of data-driven (inductive) systems modeling. In GIT, information is defined in terms of relevant uncertainty reduction. Main results regarding measures of uncertainty and uncertainty-based information in Dempster-Shafer theory of evidence and in generalized possibility theory are overviewed, and their role in three basic uncertainty principles is discussed: the principles of maximum uncertainty, minimum uncertainty, and uncertainty invariance. Finally, some open problems and undeveloped areas in GIT are examined.
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