Analyzing a class of decision problems: neural network based approach

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model, influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating an influence diagram is known to require much time and effort, and 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 DCA is introduced. DCA under consideration is viewed as a classification problem where a set of input-output data pairs is given. We thus propose a method utilizing a feedforward neural network with a supervised learning rule to develop DCA based on an influence diagram. We also examine the results of neural net simulation with an example of a class of decision problems