Sampling multisensory information and taking the appropriate motor action is critical for a biological organism’s survival, but a difficult task for robots. We present a Neurally...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Current technologies aimed at supporting processes – whether it is a business or learning process – primarily follow a metadata- and data-centric paradigm. Whereas process met...
Abstract-- In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different obje...
Abstract. We consider the case where inconsistencies are present between a system and its corresponding model, used for automatic verification. Such inconsistencies can be the resu...