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AIPS
2006
15 years 3 months ago
Probabilistic Planning with Nonlinear Utility Functions
Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...
Yaxin Liu, Sven Koenig
ICASSP
2011
IEEE
14 years 5 months ago
Contour-based hidden Markov model to segment 2D ultrasound images
The segmentation of ultrasound images is challenging due to the difficulty of appropriate modeling of their appearance variations including speckle as well as signal dropout. We ...
Xiaoning Qian, Byung-Jun Yoon
CVPR
2010
IEEE
15 years 5 months ago
Ray Markov Random Fields for Image-Based 3D Modeling: Model and Efficient Inference
In this paper, we present an approach to multi-view image-based 3D reconstruction by statistically inversing the ray-tracing based image generation process. The proposed algorithm...
Shubao Liu, David Cooper
ICML
2008
IEEE
16 years 2 months ago
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Finale Doshi, Joelle Pineau, Nicholas Roy
GECCO
2004
Springer
142views Optimization» more  GECCO 2004»
15 years 7 months ago
Improving MACS Thanks to a Comparison with 2TBNs
Abstract. Factored Markov Decision Processes is the theoretical framework underlying multi-step Learning Classifier Systems research. This framework is mostly used in the context ...
Olivier Sigaud, Thierry Gourdin, Pierre-Henri Wuil...