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AIPS
2006
14 years 11 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
78
Voted
ANOR
2005
81views more  ANOR 2005»
14 years 9 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»
14 years 9 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
89
Voted
MICCAI
2002
Springer
15 years 10 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
83
Voted
UAI
2008
14 years 11 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