A Role for Sleep in Artificial Cognition through Deferred Restructuring of Experience in Autonomous Machines

This paper is concerned with the exploration of the benefits that can be derived within a cognitive architecture for robots through the application of nature inspired sleep related cognitive restructuring processes. To this end, the concept of Deferred Restructuring of Experience in Autonomous Machines (DREAM) is postulated and applied in the context of the Multilevel Darwinist Brain architecture. This concept implies a series of consolidation, enhancement and internal imaging based exploration processes that can be applied over the experience, in terms of models and behavioral structures, a robot has acquired in its interaction with the world during its lifetime. The result is a re-representation of all of this experience so that the robot becomes more efficient and adaptive in its subsequent interactions with the world. A couple of simple proof of concept experiments demonstrate the capabilities of the approach.