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» Complexity of Planning with Partial Observability
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ECML
2005
Springer
14 years 2 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
IROS
2006
IEEE
121views Robotics» more  IROS 2006»
14 years 3 months ago
Planning and Acting in Uncertain Environments using Probabilistic Inference
— An important problem in robotics is planning and selecting actions for goal-directed behavior in noisy uncertain environments. The problem is typically addressed within the fra...
Deepak Verma, Rajesh P. N. Rao
AROBOTS
2008
166views more  AROBOTS 2008»
13 years 7 months ago
User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving
Abstract Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to provide improved maneuvering, powered wheelchairs ha...
Eric Demeester, Alexander Hüntemann, Dirk Van...
FLAIRS
2009
13 years 7 months ago
Dynamic Programming Approximations for Partially Observable Stochastic Games
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
Akshat Kumar, Shlomo Zilberstein
HCI
2009
13 years 7 months ago
Partially Observable Markov Decision Process (POMDP) Technologies for Sign Language Based Human-Computer Interaction
Sign language (SL) recognition modules in human-computer interaction systems need to be both fast and reliable. In cases where multiple sets of features are extracted from the SL d...
Sylvie C. W. Ong, David Hsu, Wee Sun Lee, Hanna Ku...