Computational Intelligence and Neuroscience / 2013 / Article / Tab 1

Research Article

A Functional Model of Sensemaking in a Neurocognitive Architecture

Table 1

Rules for inferring category likelihoods based on knowledge of category centroid location and an observed feature.

FeaturesRules

HUMINTIf an unknown event occurs, then the likelihood of the event belonging to a given category decreases as the distance from the category centroid increases.

IMINTIf an unknown event occurs, then the event is four times more likely to occur on a Government versus Military building if it is from category A or B. If an unknown event occurs, then the event is four times more likely to occur on a Military versus Government building if it is from category C or D.

MOVINTIf an unknown event occurs, the event is four times more likely to occur in dense versus sparse traffic if it is from category A or C. If an unknown event occurs, the event is four times more likely to occur in sparse versus dense traffic if it is from category B or D.

SIGINTIf SIGINT on a category reports chatter, then the likelihood of an event by that category is seven times as likely as an event by each other category.
If SIGINT on a category reports silence, then the likelihood of an event by that category is one-third as likely as an event by each other category.

SOCINTIf an unknown event occurs, then the likelihood of the event belonging to a given category is twice as likely if it is within that category’s boundary (represented as a colored region on the display).

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