312 search results - page 15 / 63 » Learning Partially Observable Deterministic Action Models |
95
Voted
ATAL
2010
Springer
14 years 10 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
14 years 11 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
14 years 11 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
15 years 3 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...
109
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CVPR
2008
IEEE
15 years 11 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...