  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.
