A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
A Statistical Viewpoint on the Theory of Evidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Exploiting Uncertainty and Incomplete Knowledge in Deceptive Argumentation
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Pruning belief decision tree methods in averaging and conjunctive approaches
International Journal of Approximate Reasoning
A multi-agent system of evidential reasoning for intelligence analyses
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
An OOPR-based rose variety recognition system
Engineering Applications of Artificial Intelligence
Transcriptional gene regulatory network reconstruction through cross platform gene network fusion
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Combining opinions about the order of rule execution
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Modelling uncertainty in agent programming
DALT'05 Proceedings of the Third international conference on Declarative Agent Languages and Technologies
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A sufficient condition for the equality of the plausibility and commonality measures of the Dempster-Shafer belief calculus is developed. When the condition is met, an efficient method to calculate relative plausibility is available. In particular, the method can be used to calculate the relative plausibility of atomic hypotheses and, therefore, it can be used to find the choice that maximizes this measure. The computation is efficient enough to make Dempster-Shafer practical in some domains where computational complexity would otherwise discourage its use.