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» Approximating Markov Processes by Averaging
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IJCAI
2003
15 years 7 months ago
Multiple-Goal Reinforcement Learning with Modular Sarsa(0)
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Nathan Sprague, Dana H. Ballard
DEDS
2010
130views more  DEDS 2010»
15 years 6 months ago
Message Batching in Wireless Sensor Networks - A Perturbation Analysis Approach
Abstract-- We address the problem of batching messages generated at nodes of a sensor network for the purpose of reducing communication energy at the expense of added latency. We f...
Xu Ning, Christos G. Cassandras
TRANSCI
2002
106views more  TRANSCI 2002»
15 years 5 months ago
The Stochastic Inventory Routing Problem with Direct Deliveries
Vendor managed inventory replenishment is a business practice in which vendors monitor their customers' inventories, and decide when and how much inventory should be replenis...
Anton J. Kleywegt, Vijay S. Nori, Martin W. P. Sav...
JMLR
2010
125views more  JMLR 2010»
15 years 28 days ago
Continuous Time Bayesian Network Reasoning and Learning Engine
We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a co...
Christian R. Shelton, Yu Fan, William Lam, Joon Le...
ICASSP
2008
IEEE
16 years 17 days ago
A new Particle Filtering algorithm with structurally optimal importance function
Bayesian estimation in nonlinear stochastic dynamical systems has been addressed for a long time. Among other solutions, Particle Filtering (PF) algorithms propagate in time a Mon...
Boujemaa Ait-El-Fquih, François Desbouvries