title: DEA: An architecture for goal planning and classification.
abstract: We introduce the differential efficiency algorithm, which partitions a perceptive 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 elementary unit. We validate the generality of this approach by testing it on four problems with continuous time and no reinforcement signal until the goal is reached (constrained object moves, Hanoi tower problem, animat control, and simple character recognition).
authors.affiliation: INRIA, Domaine de Voluceau Rocquencourt, 78150 Le Chesnay, France.
authors.forename: F
authors.initials: F
authors.lastname: Fleuret
authors.forename: E
authors.initials: E
authors.lastname: Brunet
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex; D003071:Cognition; D008959:Models, Neurological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results