Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
The basic idea to defend in this paper is that an adequate perception of the search space, sacrificing most of the precision, can paradoxically accelerate the discovery of the mo...
In many applications, a reduction of the amount of the original data or a representation of the original data by a small set of variables is often required. Among many techniques, ...
— In previous work we presented a multi-robot strategy for routing missions in large scenarios where network connectivity must be explicitly preserved. This strategy is founded o...
Terascale simulations produce data that is vast in spatial, temporal, and variable domains, creating a formidable challenge for subsequent analysis. Feature extraction as a data r...