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» Acting Optimally in Partially Observable Stochastic Domains
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AIPS
2000
13 years 10 months ago
On-line Scheduling via Sampling
1 We consider the problem of scheduling an unknown sequence of tasks for a single server as the tasks arrive with the goal off maximizing the total weighted value of the tasks serv...
Hyeong Soo Chang, Robert Givan, Edwin K. P. Chong
PVLDB
2010
89views more  PVLDB 2010»
13 years 7 months ago
Recognizing Patterns in Streams with Imprecise Timestamps
Large-scale event systems are becoming increasingly popular in a variety of domains. Event pattern evaluation plays a key role in monitoring applications in these domains. Existin...
Haopeng Zhang, Yanlei Diao, Neil Immerman
ATAL
2009
Springer
14 years 3 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
NIPS
2007
13 years 10 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...
CORR
2011
Springer
161views Education» more  CORR 2011»
13 years 9 days ago
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design...
Laurent Jacques, Laurent Duval, Caroline Chaux, Ga...