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273views
13 years 6 months ago
Monte Carlo Value Iteration for Continuous-State POMDPs
Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
IJRR
2010
162views more  IJRR 2010»
13 years 9 months ago
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
CDC
2008
IEEE
118views Control Systems» more  CDC 2008»
14 years 5 months ago
A density projection approach to dimension reduction for continuous-state POMDPs
Abstract— Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms ...
Enlu Zhou, Michael C. Fu, Steven I. Marcus
PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
13 years 8 months ago
Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
ICASSP
2011
IEEE
13 years 2 months ago
Bayesian reinforcement learning for POMDP-based dialogue systems
Spoken dialogue systems are gaining popularity with improvements in speech recognition technologies. Dialogue systems can be modeled effectively using POMDPs, achieving improvemen...
ShaoWei Png, Joelle Pineau