This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
As our ability to measure the world around us improves, we are quickly generating massive quantities of high-dimensional, spatial-temporal data. In this paper, we concern ourselve...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
OpenCV is a C/C++ computer vision library originally developed by Intel. It is free for commercial and research use under a BSD license. The library is cross-platform.
It is highl...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...