Modeling a class of decision problems using artificial neural networks

This paper presents an artificial neural network to build a decision model, together with a discussion about implementation of decision class analysis. In contrast to evaluating or analyzing decision problems, there has been little research to build decision models such as the influence diagram. In practice, generating an influence diagram requires much time and effort. Furthermore, the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of decision class analysis (DCA) is 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, i.e. to model similar decision problems within the same class. An influence diagram model is used to represent the decision problem. It is a good tool for knowledge representation of complex decision problems under uncertainty. After the influence diagram is briefly described and the concept of DCA is introduced, we propose a method for developing influence diagrams using a feedforward neural net. We also present the results of neural net simulation with an example of a class of decision problems.