73 search results - page 11 / 15 » Learning multi-agent state space representations |
ICML
1999
IEEE
14 years 8 months ago
1999
IEEE
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
UAI
2004
13 years 8 months ago
2004
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent cl...
ICPR
2010
IEEE
13 years 6 months ago
2010
IEEE
Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...
ICRA
2009
IEEE
14 years 2 months ago
2009
IEEE
— Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actio...
MICCAI
2010
Springer
13 years 5 months ago
2010
Springer
We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of ea...