Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
Embedded wireless networks have largely focused on openloop sensing and monitoring. To address actuation in closedloop wireless control systems there is a strong need to re-think ...
We consider the following model for fire containment. We are given an undirected graph G = (V, E) with a source vertex s where the fire starts. At each time step, the firefighters...
Abstract--For modern embedded systems in the realm of highthroughput multimedia, imaging, and signal processing, the complexity of embedded applications has reached a point where t...
Abstract. We study in this lecture the literature on mixed integer programming models and formulations for a specific problem class, namely deterministic production planning probl...