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» Model Minimization in Markov Decision Processes
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NIPS
2007
15 years 1 months ago
Bayes-Adaptive POMDPs
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
Stéphane Ross, Brahim Chaib-draa, Joelle Pi...
AIPS
2008
15 years 2 months ago
Multiagent Planning Under Uncertainty with Stochastic Communication Delays
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...
JAIR
2008
107views more  JAIR 2008»
14 years 11 months ago
Planning with Durative Actions in Stochastic Domains
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
Mausam, Daniel S. Weld
ECAI
1998
Springer
15 years 4 months ago
Optimal Scheduling of Dynamic Progressive Processing
Progressive processing allows a system to satisfy a set of requests under time pressure by limiting the amount of processing allocated to each task based on a predefined hierarchic...
Abdel-Illah Mouaddib, Shlomo Zilberstein
INFOCOM
2009
IEEE
15 years 6 months ago
Delay-Optimal Opportunistic Scheduling and Approximations: The Log Rule
—This paper considers the design of opportunistic packet schedulers for users sharing a time-varying wireless channel from the performance and the robustness points of view. Firs...
Bilal Sadiq, Seung Jun Baek, Gustavo de Veciana