An aggregation framework based on coherent lower previsions: Application to Zadeh's paradox and sensor networks

Authors:
Alessio Benavoli;Alessandro Antonucci
Affiliations:
“Dalle Molle” Institute for Artificial Intelligence (IDSIA), Galleria 2, Via Cantonale, CH-6928 Manno-Lugano, Switzerland;“Dalle Molle” Institute for Artificial Intelligence (IDSIA), Galleria 2, Via Cantonale, CH-6928 Manno-Lugano, Switzerland
Venue:
International Journal of Approximate Reasoning
Year:
2010

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Abstract

The problem of aggregating two or more sources of information containing knowledge about a common domain is considered. We propose an aggregation framework for the case where the available information is modelled by coherent lower previsions, corresponding to convex sets of probability mass functions. The consistency between aggregated beliefs and sources of information is discussed. A closed formula, which specializes our rule to a particular class of models, is also derived. Two applications consisting in a possible explanation of Zadeh's paradox and an algorithm for estimation fusion in sensor networks are finally reported.