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» Mean-Variance Optimization in Markov Decision Processes
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DATE
2007
IEEE
133views Hardware» more  DATE 2007»
15 years 7 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
14 years 11 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»
15 years 8 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
15 years 2 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»
15 years 1 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