Knowledge Integration for Uncertainty Management.

Knowledge integration is based upon gathering and aggregating all available data, information, and knowledge from theory, experience, computation and similar applications. Such a ”waste nothing” approach becomes important when the underlying theory is difficult to model, when observational data are sparse or difficult to measure, or when uncertainties are large. An inference approach is prescribed, providing common ground for many kinds of uncertainties arising from the sources of data, information and knowledge. These sources are integrated using a modified Saaty’s Analytic Hierarchy Process (AHP). A fusion physics application illustrates how to manage the uncertainties in the inference-based integration approach. Zadeh membership functions and possibility distributions contribute to this management.