Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
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We demonstrate that classes of dependencies among beliefs held with uncertainty cannot be represented in rule-based systems in a natural or efficient manner. We trace these limitations to a fundamental difference between certain and uncertain reasoning. In particular, we show that beliefs held with certainty are more modular than uncertain beliefs. We argue that the limitations of the rule-based approach for expressing dependencies are a consequence of forcing nonmodular knowledge into a representation scheme originally designed to represent modular beliefs. Finally, we describe a representation technique that is related, to the rule-based framework yet is not limited in the types of dependencies that it can represent.