Transformation of Mass Function and Joint Mass Function for Evidence Theory in the Continuous Domain

Abstract

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential-uncertain, imprecise, and occasionally inaccurate-called evidential information. Evidence theory (Dempster/ Shafer theory) provides one of the most general frameworks for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain-such as time, distance, or sensor measurements, However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat′s pioneering work, this paper provides a new combination rule, a new method for mass function2 transformation, and a new method for rendering joint mass functions which are of great utility in evidence theory in the continuous domain.