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» Acting Optimally in Partially Observable Stochastic Domains
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AAAI
2010
14 years 11 months ago
PUMA: Planning Under Uncertainty with Macro-Actions
Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan...
Ruijie He, Emma Brunskill, Nicholas Roy
CORR
2008
Springer
147views Education» more  CORR 2008»
14 years 9 months ago
A Minimum Relative Entropy Principle for Learning and Acting
This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is designed specifically for a particular...
Pedro A. Ortega, Daniel A. Braun
88
Voted
ECML
2005
Springer
15 years 3 months ago
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
Masoumeh T. Izadi, Doina Precup
76
Voted
IJCAI
2007
14 years 11 months ago
Learning from Partial Observations
We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of...
Loizos Michael
WCNC
2010
IEEE
15 years 1 months ago
Dynamic Control of Data Ferries under Partial Observations
—Controlled mobile helper nodes called data ferries have recently been proposed to bridge communications between disconnected nodes in a delay-tolerant manner. While existing wor...
Chi Harold Liu, Ting He, Kang-won Lee, Kin K. Leun...