Using ROBDDs for inference in Bayesian networks with troubleshooting as an example

Authors:
Thomas D. Nielsen;Pierre-Henri Wuillemin;Finn V. Jensen;Uffe Kjærulff
Affiliations:
Department of Computer Science, Aalborg University, Aalborg Ø, Denmark;Department of Computer Science, Aalborg University, Aalborg Ø, Denmark;Department of Computer Science, Aalborg University, Aalborg Ø, Denmark;Department of Computer Science, Aalborg University, Aalborg Ø, Denmark
Venue:
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Year:
2000

Citing 4
Cited 0

Graph-Based Algorithms for Boolean Function Manipulation

IEEE Transactions on Computers
Diagnosing multiple faults

Artificial Intelligence
A model-based approach to reactive self-configuring systems

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Decision-theoretic troubleshooting: a framework for repair and experiment

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 the 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.