Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Belief structures, possibility theory and decomposable confidence measures on finite sets
Computers and Artificial Intelligence
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
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
Belief and surprise: a belief-function formulation
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
Using possibility theory in expert systems
Fuzzy Sets and Systems
Nonmonotonic inference based on expectations
Artificial Intelligence
Inference in Possibilistic Hypergraphs
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Quantifying beliefs by belief functions: an axiomatic justification
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Possibilistic logic, preferential models, non-monotonicity and related issues
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
We give an axiomatization of confidence transfer - a known conditioning scheme - from the perspective of expectation-based inference in the sense of Gärdenfors and Makinson. Then, we use the notion of belief independence to "filter out" different proposals of possibilistic conditioning rules, all are variations of confidence transfer. Among the three rules that we consider, only Dempster's rule of conditioning passes the test of supporting the notion of belief independence. With the use of this conditioning role, we then show that we can use local computation for computing desired conditional marginal possibilities of the joint possibility satisfying the given constraints. It turns out that our local computation scheme is already proposed by Shenoy. However, our intuitions are completely different from that of Shenoy. While Shenoy just defines a local computation scheme that fits his framework of valuation-based systems, we derive that local computation scheme from Π(β) = Π(β) * Π(α) and appropriate independence assumptions, just like how the Bayesians derive their local computation scheme.