Associative Learning via Inhibitory Search

ALVIS is a reinforcement-based connectionist architecture that learns associative maps in continuous multidimensional environments. Thediscovered locations of positive and negative reinforcements arerecorded in "do be" and "don't be" subnetworks, respectively. The outputs of the subnetworks relevant to the current goalare combined and compared with the current location to produce an error vector. This vector is backpropagated through a motor-perceptual mapping network.