Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
In this paper we investigate the effect of three time-triggered system rejuvenation policies on service availability using a queuing model. The model is formulated as an extended ...
Several important network applications cannot easily scale to higher data rates without requiring focusing just on the large traffic flows. Recent works have discussed algorithmic...
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
The need for enterprise application integration projects leads to complex composite applications. For the sake of security and efficiency, consolidated access control policies for ...
Martin Wimmer, Alfons Kemper, Maarten Rits, Volkma...