An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
On the semantics of fuzzy logic
International Journal of Approximate Reasoning
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Decidability and expressiveness for first-order logics of probability
Information and Computation
Can we enforce full compositionality in uncertainty calculi?
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Inferences in probability logic
Artificial Intelligence
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
System stability and the fuzzy controller
Theoretical aspects of fuzzy control
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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A modified version of the first-order logic of probability presented in (Halpern 1990) - with probability on possible worlds - makes it possible to formulate an alternative characterisation of fuzzy sets. In this approach, fuzzy sets are no longer seen as primitive entities with an intuitive justification, but rather as structured entities emerging in a suitable logical framework. Some fuzzy techniques of practical relevance are shown to be encodable in this way. In addition, the resulting approach leads to a clearer epistemological analysis in that it clarifies the purposive nature of the kind of uncertainty that can be modelled by fuzziness.