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A measure based approach to the fusion of possibilistic and probabilistic uncertainty

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Abstract

We are interested in the problem of multi-source information fusion in the case when the information provided has some uncertainty. We note that sensor provided information generally has a probabilistic type of uncertainty whereas linguistic information typically introduces a possibilistic type of uncertainty. More generally, we are faced with a problem in which we must fuse information with different types of uncertainty. In order to provide a unified framework for the representation of these different types of uncertain information we use a set measure approach for the representation of uncertain information. We discuss a set measure representation of uncertain information. In the multi-source fusion problem, in addition to having a collection of pieces of information that must be fused, we need to have some expert provided instructions on how to fuse these pieces of information. Generally these instructions can involve a combination of linguistically and mathematically expressed directions. In the course of this work we begin to consider the fundamental task of how to translate these instructions into formal operations that can be applied to our information. This requires us to investigate the important problem of the aggregation of set measures.

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  1. Machine Intelligence Institute, Iona College, New Rochelle, NY, 10801, USA

    Ronald R. Yager

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  1. Ronald R. Yager
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Yager, R.R. A measure based approach to the fusion of possibilistic and probabilistic uncertainty. Fuzzy Optim Decis Making 10, 91–113 (2011). https://doi.org/10.1007/s10700-011-9098-1

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