Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

We propose a new message-passing belief propagation method that approximates belief updating on evidential networks with conditional belief functions. By means of local conditioning, the method is able to propagate beliefs on the original multiply-connected network structure using local computations, facilitating reasoning in a distributed and dynamic context. Further, by use of conditional belief functions in the form of partially defined plausibility and basic plausibility assignment functions, belief updating can be efficiently approximated using only partial information of the belief functions involved. Experiments show that the method produces results with high degree of accuracy whilst achieving a significant decrease in computational and space complexity (compared to exact methods).