Hierarchical DSmP transformation for decision-making under uncertainty

Abstract : Dempster-Shafer evidence theory is widely used for approximate reasoning under uncertainty; however, the decision- making is more intuitive and easy to justify when made in the probabilistic context. Thus the transformation to approx- imate a belief function into a probability measure is crucial and important for decision-making based on evidence theory framework. In this paper we present a new transformation of any general basic belief assignment (bba) into a Bayesian belief assignment (or subjective probability measure) based on new proportional and hierarchical principle of uncertainty reduction. Some examples are provided to show the rationality and efficiency of our proposed probability transformation approach.
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Communication dans un congrès
Fusion 2012 - 15th International Conference on Information Fusion, Jul 2012, Singapour, Singapore
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https://hal.archives-ouvertes.fr/hal-00720442
Contributeur : Jean Dezert <>
Soumis le : mardi 24 juillet 2012 - 15:15:01
Dernière modification le : jeudi 7 février 2019 - 17:10:24
Document(s) archivé(s) le : jeudi 25 octobre 2012 - 03:35:07

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HDSmPFusion2012.pdf
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  • HAL Id : hal-00720442, version 1

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Jean Dezert, Deqiang Han, Zhun-Ga Liu, Jean-Marc Tacnet. Hierarchical DSmP transformation for decision-making under uncertainty. Fusion 2012 - 15th International Conference on Information Fusion, Jul 2012, Singapour, Singapore. 〈hal-00720442〉

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