An Integrated Approach for Fuzzy Multi-Entity Bayesian Networks and Semantic Analysis for Soft and Hard Data Fusion

摘要

In this paper, a soft+hard data fusion model is proposed that is capable of combining the data generated from human-based sources with those generated by physical sensors. The basis of this model is our previously introduced Fuzzy extension to the Mutli-Entity Bayesian Network (MEBN) language, which is a High-Level Information Fusion (HLIF) framework capable of expressing the semantic and causal relationships between the entities constituting a world model, as well as managing their ambiguity and uncertainty. In our proposed model, the unstructured soft data is presented by undergoing a novel soft-data-association process, through which the data is semantically analyzed, and accurately structured in a fuzzy random variable. Moreover, the clique tree inference algorithm for Bayesian Networks is modified to handle fuzzy evidence in Fuzzy-MEBN. The simulation results, in transportation domain, show that our improved HLIF model is capable of handling both soft and hard data, and consequently, provide the user with more precise situation assessment.