Representation and fusion of Conditionally Refined opinions using evidence trees

Belief calculus provides an attractive framework to mathematically model subjective opinions of human observers. This work focuses on the situation when opinions provided by observers are on frames which are hierarchically related. The goal is to develop a scheme for facilitating the representation and fusion of such complex opinions in a computationally tractable way. A framework based on tree structure is proposed where, unlike in existing works in the literature, every node in itself is a frame of varied refinement and not a subset of a fixed frame. Algorithms for belief mass propagation down the tree are developed. The proposed representation is shown to be applicable to various soft-soft and hard-soft fusion situations. Using the advantages of the organization of the tree, all belief combination calculations are performed using small frames and later combined together by a simple concatenation operation making the proposed scheme a computationally attractive framework.