Any Time Probabilistic Reasoning for Sensor Validation
  For many real time applications, it is important to validate the information
received from the sensors before entering higher levels of reasoning. This
paper presents an any time probabilistic algorithm for validating the
information provided by sensors. The system consists of two Bayesian network
models. The first one is a model of the dependencies between sensors and it is
used to validate each sensor. It provides a list of potentially faulty sensors.
To isolate the real faults, a second Bayesian network is used, which relates
the potential faults with the real faults. This second model is also used to
make the validation algorithm any time, by validating first the sensors that
provide more information. To select the next sensor to validate, and measure
the quality of the results at each stage, an entropy function is used. This
function captures in a single quantity both the certainty and specificity
measures of any time algorithms. Together, both models constitute a mechanism
for validating sensors in an any time fashion, providing at each step the
probability of correct/faulty for each sensor, and the total quality of the
results. The algorithm has been tested in the validation of temperature sensors
of a power plant.
