Learning to Anticipate the Movements of Intermittently Occluded Objects

In English A model of event driven anticipatory learning is described and applied to a number of
attention situations where one or several visual
targets need to be tracked while being
intermittently occluded. The model combines
covert tracking of multiple targets with overt
control of a single attention focus. The implemented
system has been applied to both a
simple scenario with a car that is occluded in a
tunnel and a complex situation with six simulated
robots that need to anticipate the movements
of each other. The system is shown to
learn very quickly to anticipate target movements.
The performance is further increased
when the simulated robots are allowed to cooperate
in the tracking task.