An efficient and practical diagnosis model

Abstract

The task of diagnosis, a typical abductive problem, as to find a hypothesis that best explains a set of observations. Generally, a neural network diagnostic reasoning model finds only one hypothesis to a set of observations. It is computationally expensive to find the hypothesis because the number of the potential hypotheses is exponentially large. Recently, we have proposed a connectionist diagnosis model to overcome the above difficulty. In this paper, we propose a method to improve the efficiency and the practicality of the model. The improved model can find more solutions, and the efficiency of the model is also improved.