MODELING A CLASS OF DECISION-PROBLEMS USING ARTIFICIAL NEURAL NETWORKS

This paper presents an artificial neural network to build a decision m odel, together with a discussion about implementation of decision clas s analysis. In contrast to evaluating or analyzing decision problems, there has been little research to build decision models such as the in fluence diagram. In practice, generating an influence diagram requires much time and effort Furthermore, the resulting model can be generall y applicable to only a specific decision problem. In order to reduce t he burden of modeling decision problems, the concept of decision class analysis (DCA) is proposed. DCA treats a set of decision problems hav ing 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 d iagram model is used to represent the decision problem. It is a good t ool for knowledge representation of complex decision problems under un certainty. After the influence diagram is briefly described and the co ncept of DCA is introduced, we propose a method for developing influen ce diagrams using a feedforward neural net. We also present the result s of neural net simulation with an example of a class of decision prob lems. (C) 1997 Elsevier Science Ltd.