Introduction to Bayesian Networks
Introduction to Bayesian Networks
A comparison of decision alaysis and expert rules for sequential diagnosis
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
The SACSO methodology for troubleshooting complex systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
When to test? Troubleshooting with postponed system test
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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.