Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
This work addresses efficiency in troubleshooting for man-made machinery, but it is incremental as it adapts existing Boolean function methods to a specific domain.
The paper tackles the problem of belief updating in Bayesian networks with deterministic components by representing them as Boolean functions and using ROBDDs for inference, achieving a substantial speed-up compared to traditional junction tree propagation.
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In this paper we explore how advances in the calculation of Boolean functions can be adopted for belief updating, in particular within the context of troubleshooting. We present experimental results indicating a substantial speed-up compared to traditional junction tree propagation.