Approximations for efficient computation in the theory of evidence

摘要

The theory of evidence has become a widely used method for handling uncertainty in intelligent systems. The method has, however, an efficiency problem. To solve this problem there is a need for approximations. In this paper an approximation method in the theory of evidence is presented. Further, it is compared experimentally with Bayesian and consonant approximation methods with regard to the error they make. Depending on parameters and the nature of evidence the experiments show that the new method gives comparatively good results. Properties of the approximation methods for presentation purposes are also discussed.