Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A valuation-based language for expert systems
International Journal of Approximate Reasoning
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
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This paper deals with the knowledge representation and reasoning in directed belief networks. These networks are similar to those defined by Pearl (causal networks), but instead of probability functions, we use belief functions. Based on the work of Cano et al. [1992] in which they have presented an axiomatic framework for propagating valuations in directed acyclic graph using Shafer-Shenoy's axioms of valuation-based system (VBS), we show how the Dempster-Shafer theory fits in this framework. Then, we present a propagation algorithm in directed belief networks that is extended from Pearl's algorithm, but it is expressed in terms of belief functions.