We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
We address the problem of automatically learning object models for recognition and pose estimation. In contrast to the traditional approach, we formulate the recognition problem a...
Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithmse.g., clustering, conditioning ar...
In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which ...
We investigate the problem of modeling node capture attacks in heterogeneous wireless ad hoc and mesh networks. Classical adversarial models such as the Dolev–Yao model are know...