On detection networks and iterated influence diagrams: application to a parallel distributed structure

For two decades, detection networks of various structures have been used to study information fusion from multiple sensors and/or decision makers. On the other hand, influence diagrams are widely accepted as graphical representations for decision problems under uncertainty. In this paper, the similarities between these two modeling techniques, as well as their advantages and disadvantages are discussed using a parallel network structure as an example paradigm. A framework, termed iterated influence diagrams, which combines influence diagrams and person-by-person optimization, is proposed to take advantage of the benefits from both representations. The key purpose of the iterated influence diagrams is the relaxation of one of the major constraints of a regular influence diagram, viz., decision nodes must be ordered. As a consequence, influence diagram can also be used to represent and solve distributed detection problems, i.e., find the optimal decision policies for all the decision makers.