Abstract
It is not an easy task to obtain decisions from raw data; therefore, we designed an information fusion and probabilistic decision making framework (IFPDM). It comprises two phases and each phase consists of several layers. The first phase goes up from raw data to belief computation. In this phase, the bottom layer consists of multiple sources of textual data. Then a multi-agent system (MAS), platform is utilised to afford five agents. These agents are organised in a common platform to provide communication, data integration, parser, semantic network and belief computation agents, respectively. The second phase of IFPDM consists of four layers. These layers comprise beliefs combination, pignistic probability computation, probabilistic reasoning and decision ranking, respectively. A case study in which a divide and conquer approach is investigated to prove the concept of IFPDM. It is planned to extend IFPDM to deal with ontology, qualitative beliefs and dynamic Bayesian networks.
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