DEA: An architecture for goal planning and classification

We introduce the differential efficiency algorithm, which partitions a perc eptive space during unsupervised learning into categories and uses them to solve goal-planning and classification problems. This algorithm is inspired by a biological model of the cortex proposing the cortical column as an el ementary unit. We validate the generality of this approach by testing it on four problems with continuous time and no reinforcement signal until the g oal is reached (constrained object moves, Hanoi tower problem, animal contr ol, and simple character recognition).