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An Aggregation Framework Based on Coherent Lower Previsions: Application to Zadeh's Paradox and Sensor
 

Summary: An Aggregation Framework Based on Coherent Lower
Previsions: Application to Zadeh's Paradox and Sensor
Networks
Alessio Benavoli, Alessandro Antonucci
"Dalle Molle" Institute for Artificial Intelligence (IDSIA),
Galleria 2, Via Cantonale, CH-6928 Manno-Lugano (Switzerland)
Abstract
The problem of aggregating two or more sources of information containing knowl-
edge about a common domain is considered. We propose an aggregation frame-
work for the case where the available information is modelled by coherent lower
previsions, corresponding to convex sets of probability mass functions. The con-
sistency 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.
Keywords: Information fusion, coherent lower previsions, linear-vacuous
mixtures, independent natural extension, natural extension, generalized Bayes
rule, aggregation rule.
1. Introduction
In practical problems where modeling and handling knowledge is required,

  

Source: Antonucci, Alessandro - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)

 

Collections: Computer Technologies and Information Sciences