Design of a Framework to Facilitate Decisions Using Information Fusion
Military Technical College（军事技术学院）；Arab Academy for Information Systems（阿拉伯信息系统学院）；Azhar University（爱资哈尔大学）
Tamer M. Abo Neama, Ismail A. Ismail, Tarek S. Sobh, M. Zaki
备注：
17 pages, 5 figures, Journal of Al Azhar University Engineering Sector, Vol. 8, No. 28, July 2013, 1237-1250. arXiv admin note: text overlap with arXiv:cs/0409007 by other authors
发表信息：
Journal of Al Azhar University Engineering Sector, Vol. 8, No. 28, July 2013, 1237-1250
Information fusion is an advanced research area which can assist decision makers in enhancing their decisions. This paper aims at designing a new multi-layer framework that can support the process of performing decisions from the obtained beliefs using information fusion. Since it is not an easy task to cross the gap between computed beliefs of certain hypothesis and decisions, the proposed framework consists of the following layers in order to provide a suitable architecture (ordered bottom up): 1. A layer for combination of basic belief assignments using an information fusion approach. Such approach exploits Dezert-Smarandache Theory, DSmT, and proportional conflict redistribution to provide more realistic final beliefs. 2. A layer for computation of pignistic probability of the underlying propositions from the corresponding final beliefs. 3. A layer for performing probabilistic reasoning using a Bayesian network that can obtain the probable reason of a proposition from its pignistic probability. 4. Ranking the system decisions is ultimately used to support decision making. A case study has been accomplished at various operational conditions in order to prove the concept, in addition it pointed out that: 1. The use of DSmT for information fusion yields not only more realistic beliefs but also reliable pignistic probabilities for the underlying propositions. 2. Exploiting the pignistic probability for the integration of the information fusion with the Bayesian network provides probabilistic inference and enable decision making on the basis of both belief based probabilities for the underlying propositions and Bayesian based probabilities for the corresponding reasons. A comparative study of the proposed framework with respect to other information fusion systems confirms its superiority to support decision making.
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