High-level information fusion with Bayesian semantics
Paulo Costa, Kathryn Blackmond Laskey, Kuo‐Chu Chang, Wei Sun, Cheol Young Park, Shou Matsumoto · 2012
In an increasingly interconnected world information comes from various sources, usually with distinct, sometimes inconsistent semantics. Transforming raw data into high-level information fusion (HLIF) products, such as situation displays, automated decision support, and predictive analysis, relies heavily on human cognition. There is a clear lack of automated solutions for HLIF, making such systems prone to scalability issues. In this paper, we propose to address this issue with the use of highly expressive Bayesian models, which can provide a tighter link between information coming from low-level sources and the high-level information fusion systems, and allow for greater automation of the overall process. We illustrate our ideas with a naval HLIF system, and show the results of a preliminary set of experiments. 1