In this paper we present a computational approach to developing effective training systems for virtual simulation environments. In particular, we focus on a Naval simulation syste...
Monica N. Nicolescu, Ryan E. Leigh, Adam Olendersk...
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we c...
This paper presents a new approach for incremental 3D SLAM from segmented range images with unknown feature association. For feature and motion tracking, an any-time interpretation...
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...