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ATAL
2006
Springer
15 years 1 months ago
Learning executable agent behaviors from observation
We present a method for learning a human understandable, executable model of an agent's behavior using observations of its interaction with the environment. By executable we ...
Andrew Guillory, Hai Nguyen, Tucker R. Balch, Char...
ICML
2010
IEEE
14 years 7 months ago
Approximate Predictive Representations of Partially Observable Systems
We provide a novel view of learning an approximate model of a partially observable environment from data and present a simple implemenf the idea. The learned model abstracts away ...
Monica Dinculescu, Doina Precup
ICRA
2002
IEEE
147views Robotics» more  ICRA 2002»
15 years 2 months ago
Auxiliary Particle Filter Robot Localization from High-Dimensional Sensor Observations
We apply the auxiliary particle filter algorithm of Pitt and Shephard (1999) to the problem of robot localization. To deal with the high-dimensional sensor observations (images) ...
Nikos A. Vlassis, Bas Terwijn, Ben J. A. Krös...
ISQED
2007
IEEE
165views Hardware» more  ISQED 2007»
15 years 4 months ago
On-Line Adjustable Buffering for Runtime Power Reduction
We present a novel technique to exploit the power-performance tradeoff. The technique can be used stand-alone or in conjunction with dynamic voltage scaling, the mainstream techn...
Andrew B. Kahng, Sherief Reda, Puneet Sharma
ECML
2005
Springer
15 years 3 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup