Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Reasoning under incomplete information in artificial intelligence: a comparison of formalisms using a single example
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
A System of Relational Syllogistic Incorporating Full Boolean Reasoning
Journal of Logic, Language and Information
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In this paper we present a new approach to a symbolic treatment of quantified statements having the following form “iQ iA's are iB's”, knowing that iA and iB are labels denoting sets, and iQ 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 iM-valued logic in which the graduation scale of iM symbolic quantifiers is translated in terms of truth degrees. Moreover, we propose symbolic inference rules allowing us to manage quantified statements.