@article{DEKLEER1990381,
title = {Using crude probability estimates to guide diagnosis},
journal = {Artificial Intelligence},
volume = {45},
number = {3},
pages = {381-391},
year = {1990},
issn = {0004-3702},
doi = {https://doi.org/10.1016/0004-3702(90)90012-O},
url = {https://www.sciencedirect.com/science/article/pii/000437029090012O},
author = {Johan {de Kleer}},
abstract = {In order to identify the faulty components of a malfunctioning device in the fewest number of measurements, model-based diagnosis often uses a minimum entropy technique to select the next best measurement. This technique seems critically dependent on the availability of failure probabilities for components. Unfortunately, in many cases this information is unavailable or unknown. However, if we can assume that all components fail independently with equal probability and that components fail with very small probability, then it is possible to exploit the intuitions of the technique even when the exact probabilities are unknown. In addition, the computation required is much simpler. This approach can be generalized if the set of components can be partitioned such that each of the components of a partition fail with equal probability but are much more or less likely to fail than those of other partitions.}
}