An integral with respect to probabilistic-valued decomposable measures

Several concepts of approximate reasoning in uncertainty processing are linked to the processing of distribution functions. In this paper we make use of probabilistic framework of approximate reasoning by proposing a Lebesgue-type approach to integration of non-negative real-valued functions with respect to probabilistic-valued decomposable (sub)measures. Basic properties of the corresponding probabilistic integral are investigated in detail. It is shown that certain properties, among them linearity and additivity, depend on the properties of the underlying triangle function providing (sub)additivity condition of the considered (sub)measure. It is demonstrated that the introduced integral brings a new tool in approximate reasoning and uncertainty processing with possible applications in several areas.Comment: 17 pages, 2 metapost figures; v2 substantially rewritten version including application to Moore's interval analysis, 19 pages, 2 metapost figure

Probabilistic-valued decomposable set functions with respect to triangle functions

In the framework of the generalized measure theory the decomposable probabilistic-valued set functions are introduced with triangle functions $\tau$ in an appropriate probabilistic metric space as natural candidates for the "addition", leading to the concept of $\tau$-decomposable measures. Several set functions, among them the classical (sub)measures, previously defined $\tau_T$-submeasures, $\tau_{L,A}$-submeasures as well as recently introduced Shen's (sub)measures are described and investigated in a unified way. Basic properties and characterizations of $\tau$-decomposable (sub)measures are also studied and numerous extensions of results from the above mentioned papers are provided.Comment: v2 rewritten substantially, 14 page