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We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value...
The current growth of smart grid capable appliances motivates the development of general and flexible software systems to support these devices. The FlexiblePower Application Infrastructure (FPAI) is such a system, which classifies devices by their type of flexibility. Subsequently, energy applications only have to support these flexibility classes. In this work, we present an implementation of the...
This paper addresses the optimal planning problem on register automata, a special class of finite state machines which can process continuous inputs. Register automata emerge as abstractions of a class of switched dynamical systems with convergent continuous component dynamics, and dwell times sufficiently large to allow convergence to neighborhoods of parameterized limit sets in finite time. A word...
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