Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
Many artificial intelligence techniques rely on the notion ate" as an abstraction of the actual state of the nd an "operator" as an abstraction of the actions that ...
Abstract. The subject of this paper is nding small sample spaces for joint distributions of n discrete random variables. Such distributions are often only required to obey a certa...
We present a novel approach to the problem of the indoor localization in wireless environments. The main contribution of this paper is four folds: (a) We show that, by projecting t...
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solu...