312 search results - page 15 / 63 » Learning Partially Observable Deterministic Action Models |
ATAL
2010
Springer
13 years 8 months ago
2010
Springer
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
UAI
2001
13 years 9 months ago
2001
There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...
UAI
2008
13 years 9 months ago
2008
Approximate inference in dynamic systems is the problem of estimating the state of the system given a sequence of actions and partial observations. High precision estimation is fu...
COLT
2007
Springer
14 years 1 months ago
2007
Springer
In the standard model of observational learning, n agents sequentially decide between two alternatives a or b, one of which is objectively superior. Their choice is based on a stoc...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human...