A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Fast Algorithms for Dempster-Shafer Theory
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Computational aspects of the Mobius transformation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Computational methods for a mathematical theory of evidence
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Hi-index | 0.00 |
In this article we present two ways of structuring bodies of evidence, which allow us to reduce the complexity of the operations usually performed in the framework of evidence theory. The first structure just partitions the focal elements in a body of evidence by their cardinality. With this structure we are able to reduce the complexity on the calculation of the belief functions Bel, Pl, and Q. The other structure proposed here, the Hierarchical Trees, permits us to reduce the complexity of the calculation of Bel, Pl, and Q, as well as of the Dempster's rule of combination in relation to the brute-force algorithm. Both these structures do not require the generation of all the subsets of the reference domain.