Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Reasoning under incomplete information in artificial intelligence: a comparison of formalisms using a single example
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
A symbolic approach to reasoning with linguistic quantifiers
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A qualitative theory of uncertainty
Fundamenta Informaticae
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Imprecise Quantifiers and Conditional Probabilities
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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In this paper we present a new approach to a symbolic treatment of quantified statements having the following form "Q A's are B's", knowing that A and B are labels denoting sets, and Q is a linguistic quantifier interpreted as a proportion evaluated in a qualitative way. Our model can be viewed as a symbolic generalization of statistical conditional probability notions as well as a symbolic generalization of the classical probabilistic operators. Our approach is founded on a symbolic finite M-valued logic in which the graduation scale of M symbolic quantifiers is translated in terms of truth degrees of a particular predicate. Then, we present symbolic syllogisms allowing us to deal with quantified statements.