The role of fuzzy logic in the management of uncertainty in expert systems
Fuzzy Sets and Systems
Information Sciences: an International Journal
Paper: Rating and ranking of multiple-aspect alternatives using fuzzy sets
Automatica (Journal of IFAC)
Fuzzy subsethood and belief functions of fuzzy events
Fuzzy Sets and Systems
Joint propagation of probability and possibility in risk analysis: Towards a formal framework
International Journal of Approximate Reasoning
On Some Mathematical Structures of T-Fuzzy Rough Set Algebras in Infinite Universes of Discourse
Fundamenta Informaticae - Advances in Rough Set Theory
Comparison Of Rough-Set And Interval-Set Models For Uncertain Reasoning
Fundamenta Informaticae
Compact versus noncompact LP formulations for minimizing convex Choquet integrals
Discrete Applied Mathematics
Logics for belief functions on MV-algebras
International Journal of Approximate Reasoning
Inferential processes leading to possibility and necessity
Information Sciences: an International Journal
Qualitative capacities as imprecise possibilities
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Multi-argument fuzzy measures on lattices of fuzzy sets
Knowledge-Based Systems
Do inferential processes affect uncertainty frameworks?
Fuzzy Sets and Systems
| Hi-index | 22.14 |
An organized body of results pertaining to the analysis of evidence, when the available knowledge is pervaded with imprecision, is provided. The evidence theory of Dempster and Shafer is extended to the case of fuzzy observations and fuzzy events. Upper and lower possibilities of such events are derived by iterating the generation process of upper and lower probabilities, as done by Dempster. Both probabilistic and ''possibilistic'' models are developed in parallel. These evidence measures are used for decision evaluation when the available knowledge is poor. The classical model of decision-making under uncertainty is thus extended to the case when the consequences of a decision are only roughly described and their probabilities of occurrence modeled by intervals or fuzzy numbers.