Troubleshooting with Simultaneous Models

Abstract

The goal of decision-theoretic troubleshooting is to find a sequence of actions that minimizes the expected cost of repair of a device. If the device is complex then it is convenient to create several Bayesian Networks, each designed to solve a particular problem. At the beginning of a troubleshooting process, it is often necessary to help the user to select the proper model. Complications arise if the user is able to give only a vague description of the problem. In such a case we need to work simultaneously with many troubleshooting models. In this paper we show how models that were originally designed as independent models can be used together while memory space and computational time are kept low. We allow models to be overlapping, i.e., two or more models may contain equivalent troubleshooting steps and/or equivalent problem causes (device faults). We propose a troubleshooting procedure that can be used with many simultaneous models at once. The key that enables us to join the models together is the single fault assumption, which means that there is only one fault causing a device malfunction at a time.