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» Mean-Variance Optimization in Markov Decision Processes
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DATE
2007
IEEE
133views Hardware» more  DATE 2007»
14 years 2 months ago
Stochastic modeling and optimization for robust power management in a partially observable system
As the hardware and software complexity grows, it is unlikely for the power management hardware/software to have a full observation of the entire system status. In this paper, we ...
Qinru Qiu, Ying Tan, Qing Wu
GLOBECOM
2010
IEEE
13 years 5 months ago
Cooperative Relay Scheduling under Partial State Information in Energy Harvesting Sensor Networks
Abstract--Sensors equipped with energy harvesting and cooperative communication capabilities are a viable solution to the power limitations of Wireless Sensor Networks (WSNs) assoc...
Huijiang Li, Neeraj Jaggi, Biplab Sikdar
IROS
2009
IEEE
206views Robotics» more  IROS 2009»
14 years 2 months ago
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Patrick Dallaire, Camille Besse, Stéphane R...
AAAI
2000
13 years 9 months ago
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
We propose a frameworkfor robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGolog model a...
Craig Boutilier, Raymond Reiter, Mikhail Soutchans...
CORR
2008
Springer
103views Education» more  CORR 2008»
13 years 7 months ago
Quickest Change Detection of a Markov Process Across a Sensor Array
Recent attention in quickest change detection in the multi-sensor setting has been on the case where the densities of the observations change at the same instant at all the sensor...
Vasanthan Raghavan, Venugopal V. Veeravalli