Abstract
We discuss how the Dempster-Shafer belief structure provides a framework for modeling an uncertain value x˜ from some domain X. We note how it involves a two-step process: the random determination of one focal element (set) guided by a probability distribution and then the selection of x˜ from this focal element in some unspecified manner. We generalize this framework by allowing the selection of the focal element to be determined by a random experiment guided by a fuzzy measure. In either case the anticipation that x˜ lies in some subset E is interval-valued, [Bel(E), Pl(E)]. We next look at database retrieval and turn to issue of determining if a database entity with an uncertain attribute value satisfies a desired value. Here we model our uncertain attribute value as x˜ and our desired value as a subset E. In this case the degree of satisfaction of the query E by the entity is [Bel(E), Pl(E)]. In order to compare these interval-valued satisfactions we use the Golden rule representative value to turn the intervals into scalars. We describe an application involving retrieval from a uncertain database.