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» On Using Monte Carlo Methods for Scheduling
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ML
2006
ACM
113views Machine Learning» more  ML 2006»
13 years 7 months ago
Learning to bid in bridge
Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In additio...
Asaf Amit, Shaul Markovitch
PAMI
2006
183views more  PAMI 2006»
13 years 7 months ago
A Model-Based Approach for Estimating Human 3D Poses in Static Images
Estimating human body poses in static images is important for many image understanding applications including semantic content extraction and image database query and retrieval. Th...
Mun Wai Lee, Isaac Cohen
INFFUS
2010
121views more  INFFUS 2010»
13 years 6 months ago
The statistical mechanics of belief sharing in multi-agent systems
- Many exciting, emerging applications require that a group of agents share a coherent view of the world given spatial distribution, incomplete and uncertain sensors, and communica...
Robin Glinton, Katia P. Sycara, David Scerri, Paul...
TSMC
2011
258views more  TSMC 2011»
13 years 2 months ago
Cross-Entropy Optimization of Control Policies With Adaptive Basis Functions
—This paper introduces an algorithm for direct search of control policies in continuous-state discrete-action Markov decision processes. The algorithm looks for the best closed-l...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
CVPR
2001
IEEE
14 years 9 months ago
Texture Replacement in Real Images
Texture replacement in real images has many applications, such as interior design, digital movie making and computer graphics. The goal is to replace some specified texture patter...
Yanghai Tsin, Yanxi Liu, Visvanathan Ramesh