Sciweavers

92 search results - page 4 / 19
» Acting Optimally in Partially Observable Stochastic Domains
Sort
View
AAAI
2010
15 years 1 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 11 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
ECML
2005
Springer
15 years 5 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
IJCAI
2007
15 years 1 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
88
Voted
WCNC
2010
IEEE
15 years 3 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...