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NIPS
1996
13 years 9 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
CCO
2001
Springer
161views Combinatorics» more  CCO 2001»
14 years 4 days ago
Branch, Cut, and Price: Sequential and Parallel
Branch, cut, and price (BCP) is an LP-based branch and bound technique for solving large-scale discrete optimization problems (DOPs). In BCP, both cuts and variables can be generat...
Laszlo Ladányi, Ted K. Ralphs, Leslie E. Tr...
WSC
2000
13 years 9 months ago
Productivity modeling of semiconductor manufacturing equipment
Optimizing a semiconductor wafer fab requires balancing technology and productivity. Recent work on productivity modeling will be described and focus on discrete event simulation ...
Mark Pool, Robert Bachrach
CSDA
2006
71views more  CSDA 2006»
13 years 7 months ago
Practical representations of incomplete probabilistic knowledge
This article deals with the compact representation of incomplete probabilistic knowledge which can be encountered in risk evaluation problems, for instance in environmental studie...
Cédric Baudrit, Didier Dubois
SCP
2008
86views more  SCP 2008»
13 years 7 months ago
Quantifying the yield of risk-bearing IT-portfolios
We proposed a method to quantify the yield of an IT-investment portfolio in an environment of uncertainty and risk. For various common implementation scenarios such as growing dem...
R. J. Peters, Chris Verhoef