Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
In this paper we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have bee...
The paper considers robust optimization to cope with uncertainty about the stock return process in one period option hedging problems. The robust approach relates portfolio choice ...
We consider the robust beamforming problem under imperfect channel state information (CSI) subject to SINR constraints in a downlink multiuser MISO system. One popular approach to...
Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute a generic and expressive framework for multiagent planning under uncertainty. However, plannin...
Frans A. Oliehoek, Shimon Whiteson, Matthijs T. J....