Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
This paper considers a scenario in which a secondary user makes opportunistic use of a channel allocated to some primary network. The primary network operates in a time-slotted ma...
Anh Tuan Hoang, Ying-Chang Liang, David Tung Chong...
We analyze the asymptotic behavior of agents engaged in an infinite horizon partially observable stochastic game as formalized by the interactive POMDP framework. We show that whe...
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an age...
The paper presents an efficient solution to decision problems where direct partial information on the distribution of the states of nature is available, either by observations of ...