An introduction to bipolar representations of information and preference 论文
2008International Journal of Intelligent Systems引用 229
Multi-Criteria Decision MakingBayesian Modeling and Causal InferenceRough Sets and Fuzzy Logic
摘要
Bipolarity seems to pervade human understanding of information and preference, and bipolar representations look very useful in the development of intelligent technologies. Bipolarity refers to an explicit handling of positive and negative sides of information. Basic notions and background on bipolar representations are provided. Three forms of bipolarity are laid bare: symmetric univariate, dual bivariate, and asymmetric (or heterogeneous) bipolarity. They can be instrumental in the logical handling of incompleteness and inconsistency, rule representation and extraction, argumentation, learning, and decision analysis. © 2008 Wiley Periodicals, Inc.
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