In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...

In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...

We deal with a sequencing problem that arises when there are multiple repair actions available to fix a broken man-made system and the true cause of the system failure is uncertain. The system is formally described by a probabilistic model, and it is to be repaired by a sequence of troubleshooting actions designed to identify the cause of the malfunction and fix the system. The task is to find a course of repair with minimal expected cost. We propose a binary integer programming formulation for...