Fuzzy multi entity Bayesian networks: A model for imprecise knowledge representation and reasoning in high-level information fusion

Keyvan Golestan, Fakhri Karray, Mohamed S. Kamel

Abstract: This paper presents a novel comprehensive Fuzzy extension to Multi-Entity Bayesian Networks (MEBN) that is deemed a well-studied and theoretically rich language that expressively handles semantics analysis, and effectively model uncertainty management. However, MEBN lack the capability of modeling the inherent conceptual and structural ambiguity that is delivered with the knowledge gained through human language. In this paper, Fuzzy MEBN that is a new version of MEBN which is based on First-order Fuzzy Logic, and Fuzzy Bayesian Networks is introduced. Furthermore, its applicability is evaluated by implementing an application related to Vehicular Ad-hoc Networks area. The results demonstrate that Fuzzy MEBN is capable of dealing with ambiguous semantical and uncertain causal relationships between the knowledge entities very efficiently.