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AIPS
2006
13 years 9 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
ANOR
2005
81views more  ANOR 2005»
13 years 7 months ago
Managing Stochastic, Finite Capacity, Multi-Project Systems through the Cross-Entropy Methodology
This paper addresses the problem of loading a finite capacity, stochastic (random) and dynamic multi-project system. The system is controlled by keeping a constant number of projec...
Izack Cohen, Boaz Golany, Avraham Shtub
CORR
2008
Springer
107views Education» more  CORR 2008»
13 years 7 months ago
Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm
As an example of the recently introduced concept of rate of innovation, signals that are linear combinations of a finite number of Diracs per unit time can be acquired by linear fi...
Vincent Yan Fu Tan, Vivek K. Goyal
MICCAI
2002
Springer
14 years 8 months ago
Stochastic Finite Element Framework for Cardiac Kinematics Function and Material Property Analysis
Abstract. A stochastic finite element method (SFEM) based framework is proposed for the simultaneous estimation of cardiac kinematics functions and material model parameters. While...
Pengcheng Shi, Huafeng Liu
UAI
2008
13 years 9 months ago
Sparse Stochastic Finite-State Controllers for POMDPs
Bounded policy iteration is an approach to solving infinitehorizon POMDPs that represents policies as stochastic finitestate controllers and iteratively improves a controller by a...
Eric A. Hansen