A model for reasoning about persistence and causation
Computational Intelligence
Readings in model-based diagnosis
Readings in model-based diagnosis
Modeling uncertain temporal evolutions in model-based diagnosis
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Decision-theoretic troubleshooting
Communications of the ACM
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Representing diagnostic knowledge for probabilistic Horn abduction
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Proceedings of the 1999 ACM symposium on Applied computing
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Probabilistic model-based diagnosis computes the posterior probabilities of failure of components from the prior probabilities of component failure and observations of system behavior. One problem with this method is that such priors are almost never directly available. One of the reasons is that the prior probability estimates include an implicit notion of a time interval over which they are specified - for example, if the probability of failure of a component is 0.05, is this over the period of a day or is this over a week? A second problem facing probabilistic model-based diagnosis is the modeling of persistence. Say we have an observation about a system at time t1 and then another observation at a later time t2. To compute posterior probabilities that take into account both the observations, we need some model of how the state of the system changes from time t1 to t2. In this paper, we address these problems using techniques from Reliability theory. We show how to compute the failure prior of a component from an empirical measure of its reliability - the Mean Time Between Failure (MTBF). We also develop a scheme to model persistence when handling multiple time tagged observations.