In this paper, we propose a new paradigm for local communication between devices in Ubiquitous Computing environments, assuming a multitude of computerized everyday appliances comm...
Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to performcomposite tasks made up of several elemental tasks by reinforcement learning. Skills acqui...
Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit the changing needs of a computation during run time. The increa...
Most implementations of functional and functional logic languages treat numbers and the basic numeric operations as external entities. The main reason for this is efficiency. Howe...
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...