Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Artificial Intelligence
Qualitative reasoning with imprecise probabilities
Journal of Intelligent Information Systems - Special issue: fuzzy logic and uncertainty management in information systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Some Varieties of Qualitative Probability
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the retranslation process in Zadeh's paradigm of computing with words
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
A new version of 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
An Approach to Computing With Words Based on Canonical Characteristic Values of Linguistic Labels
IEEE Transactions on Fuzzy Systems
A Fuzzy Linguistic Methodology to Deal With Unbalanced Linguistic Term Sets
IEEE Transactions on Fuzzy Systems
Service selection in stochastic environments: a learning-automaton based solution
Applied Intelligence
A belief classification rule for imprecise data
Applied Intelligence
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In this paper, we present a new 2-tuple linguistic representation model, i.e. Distribution Function Model (DFM), for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) framework. Such new approach allows to preserve the precision and efficiency of the combination of linguistic information in the case of either equidistant or unbalanced label model. Some basic operators on imprecise 2-tuple labels are presented together with their extensions for imprecise 2-tuple labels. We also give simple examples to show how precise and imprecise qualitative information can be combined for reasoning under uncertainty. It is concluded that DSmT can deal efficiently with both precise and imprecise quantitative and qualitative beliefs, which extends the scope of this theory.