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FLAIRS
2009
13 years 5 months ago
Dynamic Programming Approximations for Partially Observable Stochastic Games
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...
Akshat Kumar, Shlomo Zilberstein
NECO
2007
127views more  NECO 2007»
13 years 7 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
Joseph F. Murray, Kenneth Kreutz-Delgado
CDC
2008
IEEE
206views Control Systems» more  CDC 2008»
14 years 2 months ago
Approximate dynamic programming using support vector regression
— This paper presents a new approximate policy iteration algorithm based on support vector regression (SVR). It provides an overview of commonly used cost approximation architect...
Brett Bethke, Jonathan P. How, Asuman E. Ozdaglar
CSCLP
2005
Springer
14 years 1 months ago
A Hybrid Benders' Decomposition Method for Solving Stochastic Constraint Programs with Linear Recourse
Abstract. We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a ...
Armagan Tarim, Ian Miguel
ICLA
2011
Springer
12 years 11 months ago
A Stochastic Interpretation of Propositional Dynamic Logic: Expressivity
We propose a probabilistic interpretation of Propositional Dynamic Logic (PDL). We show that logical and behavioral equivalence are equivalent over general measurable spaces. This...
Ernst-Erich Doberkat