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- articleJune 2007
Characterizing and reasoning about probabilistic and non-probabilistic expectation
Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the underlying ...
- ArticleAugust 2006
Relational measures and integration
RelMiCS'06/AKA'06: Proceedings of the 9th international conference on Relational Methods in Computer Science, and 4th international conference on Applications of Kleene AlgebraPages 343–357https://doi.org/10.1007/11828563_23Work in fuzzy modeling has recently made its way from the interval $[0,1]\subseteq {\mathord{\rm I \! R}}$ to the ordinal or even to the qualitative level. We proceed further and introduce relational measures and relational integration. First ideas of ...
- ArticleJuly 2005
Consonant random sets: structure and properties
ECSQARU'05: Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with UncertaintyPages 860–871https://doi.org/10.1007/11518655_72In this paper, we investigate consonant random sets from the point of view of lattice theory. We introduce a new definition of consonancy and study its relationship with possibility measures as upper probabilities. This allows us to improve a number of ...
- articleDecember 2004
A random set characterization of possibility measures
Information Sciences: an International Journal (ISCI), Volume 168, Issue 1-4Pages 51–75https://doi.org/10.1016/j.ins.2003.09.028Several authors have pointed out the relationship between consonant random sets and possibility measures. However, this relationship has only been proven for the finite case, where the inverse Möbius of the upper probability induced by the random set ...
- articleAugust 2003
Genuine sets, various kinds of fuzzy sets and fuzzy rough sets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (UFKS), Volume 11, Issue 4Pages 467–494https://doi.org/10.1142/S0218488503002193In this paper, deriving the type-m fuzzy sets, intuitionistic fuzzy sets, φ-fuzzy sets, rough sets, fuzzy rough sets and rough fuzzy sets as particular genuine sets, and establishing their connections with genuine sets, it is demonstrated that the ...
- articleJuly 2001
Plausibility measures and default reasoning
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We focus ...
- ArticleMarch 1995
Modeling temporal uncertainty in microprocessor systems
ISUMA '95: Proceedings of the 3rd International Symposium on Uncertainty Modelling and AnalysisPage 26In microprocessor system diagnosis, temporal reasoning of event changes occurring at imprecisely known time instants is an important issue. The time range approach was proposed to capture the notion of time imprecision in event occurrence. According to ...
- ArticleMarch 1995
Constructing possibility measures
ISUMA '95: Proceedings of the 3rd International Symposium on Uncertainty Modelling and AnalysisPage 472Addresses some aspects of the extension problem for possibility measures: given the values that a (fuzzy) set mapping takes on a family of (fuzzy) sets, is it possible to extend this mapping to a possibility measure? This problem is shown to be ...
- articleNovember 1989
Upper and lower fuzzy measures
Fuzzy Sets and Systems (FSTS), Volume 33, Issue 2Pages 191–200https://doi.org/10.1016/0165-0114(89)90240-6Puri and Ralescu [5] showed that a possibility measure is not, in the general case, a fuzzy measure. This paper deals with determining a general framework where both possibility and fuzzy measures are included. In order to do that, the upper (lower) ...