Study of Discounting Methods Applied to Canonical Decomposition of Belief Functions

Abstract

In Demspter-Shafer theory the discounting operation can be used to weak the belief according to the reliability of the source of information. This is usually done by modifying basic belief assignment also called mass function. The canonical decomposition- well adapted for some combination rules- is an other way to represent the belief of an agent. We propose in this paper to focus on discounting methods that are directly applied to the canonical decomposition and we compare them to methods previously studied in the literature. Then we propose an equation that implements the discounting on canonical decomposition strictly equivalent to the classical discounting method. This approach reduces the computation cost significativly. Finally, we illustrate the validity of the method by demonstrating the convergence of the distributed data fusion algorithm using an operator based on the cautious rule and discounting. using an application on a distributed data fusion algorithm, we demonstrate the convergence when the cautious operator is used.