2013/03/20 by Sandra Sandri, Sandri, Sandra
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.5746
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)
arxiv created 2013/03/20 · openalex publication_date 2013/03/20 · arxiv updated 2013/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.