Claude F. Touzet

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We present the results of a research aimed at improving the Q-learning method through the use of artificial neural networks. Neural implementations are interesting due to their generalisation ability. Two implementations are proposed: one with a competitive multilayer perceptron and the other with a self-organising map. Results obtained on a task of(More)
Most of the straight-forward learning approaches in cooperative robotics imply for each learning robot a state space growth exponential in the number of team members. To remedy the exponentially large state space, we propose to investigate a less demanding cooperation mechanism—i.e., various levels of awareness—instead of communication. We define awareness(More)
It has been proposed that visual recognition memory and certain attentional mechanisms are impaired early in Alzheimer disease (AD). Little is known about visuospatial recognition memory in AD. The crucial role of the hippocampus on spatial memory and its damage in AD suggest that visuospatial recognition memory may also be impaired early. The aim of the(More)
Robot learning is a challenging – and somewhat unique – research domain. If a robot behavior is defined as a mapping between situations that occurred in the real world and actions to be accomplished, then the supervised learning of a robot behavior requires a set of representative examples (situation, desired action). In order to be able to gather such(More)
J. Schmidhuber proposes a "theory of fun & intrinsic motivation & creativity" that he has developed over the last two decades. This theory is precise enough to allow the programming of artificial agents exhibiting the requested behaviors. Schmidhuber's theory relies on an explicit 'internal joy drive' implemented by an 'information compression indicator'.(More)