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» Learning to Control in Operational Space
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147
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NIPS
1996
15 years 5 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
124
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TSMC
2008
104views more  TSMC 2008»
15 years 3 months ago
Using Shared-Resource Capacity for Robust Control of Failure-Prone Manufacturing Systems
Deadlock-free resource allocation has been an active area of research in flexible manufacturing. Most researchers have assumed that allocated resources do not fail, and thus, littl...
Shengyong Wang, Song Foh Chew, Mark A. Lawley
110
Voted
ALGORITHMICA
2010
153views more  ALGORITHMICA 2010»
15 years 3 months ago
Confluently Persistent Tries for Efficient Version Control
We consider a data-structural problem motivated by version control of a hierarchical directory structure in a system like Subversion. The model is that directories and files can b...
Erik D. Demaine, Stefan Langerman, Eric Price
157
Voted
UAI
2000
15 years 5 months ago
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
Frank Wittig, Anthony Jameson
UIST
2004
ACM
15 years 9 months ago
The IBar: a perspective-based camera widget
We present a new screen space widget, the IBar, for comprehensive camera control. The IBar provides a compelling interface for controlling scene perspective based on the artistic ...
Karan Singh, Cindy Grimm, Nisha Sudarsanam