Continuous WOWA operators with application to defuzzification
Aggregation operators
Fusion rules for merging uncertain information
Information Fusion
A Qualitative Bipolar Argumentative View of Trust
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
International Journal of Approximate Reasoning
Journal of Systems and Software
Assessing the aggregation of parameterized imprecise classification
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
Algorithms for possibility assessments: Coherence and extension
Fuzzy Sets and Systems
Computers and Electronics in Agriculture
A framework for multiset merging
Fuzzy Sets and Systems
Parameterized imprecise classification: elicitation and assessment
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Measuring conflict between possibilistic uncertain information through belief function theory
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Measuring the quality of uncertain information using possibilistic logic
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Eliciting fuzzy distributions from experts for ranking conceptual risk model components
Environmental Modelling & Software
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
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The problem of modeling expert knowledge about numerical parameters in the field of reliability is reconsidered in the framework of possibility theory. Usually expert opinions about quantities such as failure rates are modeled, assessed, and pooled in the setting of probability theory. This approach does not seem to always be natural since probabilistic information looks too rich to be currently supplied by individuals. Indeed, information supplied by individuals is often incomplete, imprecise rather than tainted with randomness. Moreover, the probabilistic framework looks somewhat restrictive to express the variety of possible pooling modes. In this paper, the authors formulate a model of expert opinion by means of possibility distributions that are thought to better reflect the imprecision pervading expert judgments. They are weak substitutes to unreachable subjective probabilities. Assessment evaluation is carried out in terms of calibration and level of precision, respectively, measured by membership grades and fuzzy cardinality indexes. Finally, drawing from previous works on data fusion using possibility theory, the authors present various pooling modes with their formal model under various assumptions concerning the experts. A comparative experiment between two computerized systems for expert opinion analysis has been carried out, and its results are presented in this paper