Neural network-based decision class analysis for building topological-level influence diagram

Abstract

In order to reduce the burden of modeling decision problems, the concept of decision class analysis (DCA) was proposed. DCA treats a set of decision problems having some degree of similarity as a single unit. This paper presents a scheme within which a neural network is used to implement DCA. An influence diagram model is employed to represent the decision problem, since the diagram is a good tool for knowledge representation of complex decision problems under uncertainty. DCA under consideration is viewed as a classification problem where a set of input–output data pairs is given. We thus utilize a feed-forward neural network with a supervised learning procedure so as to develop DCA and then to generate an influence diagram in the topological level. This paper also presents the results of the neural net simulation with an example of a class of decision problems.