We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning me...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
Decision-theoretic reasoning and planning algorithms are increasingly being used for mobile robot navigation, due to the signi cant uncertainty accompanying the robots' perce...
We look at iterated power generators si = se i−1 mod N for a random seed s0 ∈ ZN that in each iteration output a certain amount of bits. We show that heuristically an output of...
Efficient and effective deployment of IEEE 802.16 networks to service an area of users with certain traffic demands is an important network planning problem. We resort to an evol...
Ting Hu, Yuanzhu Peter Chen, Wolfgang Banzhaf, Rob...