Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, ...
Daniel Golovin, Andreas Krause, Beth Gardner, Sara...
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 novel optimization models are proposed for planning Wireless Mesh Networks (WMNs), where the objective is to minimize the network installation cost while providing f...
Edoardo Amaldi, Antonio Capone, Matteo Cesana, Ila...
The Brain is a slow computer yet humans can skillfully play games such as tennis where very fast reactions are required. Of particular interest is the evidence for strategic thinki...
Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...