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AAAI
1998
15 years 7 months ago
Solving Very Large Weakly Coupled Markov Decision Processes
We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
162
Voted
JAIR
2000
152views more  JAIR 2000»
15 years 5 months ago
Value-Function Approximations for Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Milos Hauskrecht
192
Voted
DMIN
2009
142views Data Mining» more  DMIN 2009»
15 years 3 months ago
Action Selection in Customer Value Optimization: An Approach Based on Covariate-Dependent Markov Decision Processes
Typical methods in CRM marketing include action selection on the basis of Markov Decision Processes with fixed transition probabilities on the one hand, and scoring customers separ...
Angi Roesch, Harald Schmidbauer
CORR
2006
Springer
86views Education» more  CORR 2006»
15 years 6 months ago
Optimal Distortion-Power Tradeoffs in Sensor Networks: Gauss-Markov Random Processes
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements ...
Nan Liu, Sennur Ulukus
DSN
2006
IEEE
16 years 5 days ago
Automatic Recovery Using Bounded Partially Observable Markov Decision Processes
This paper provides a technique, based on partially observable Markov decision processes (POMDPs), for building automatic recovery controllers to guide distributed system recovery...
Kaustubh R. Joshi, William H. Sanders, Matti A. Hi...