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ICML
2010
IEEE
13 years 2 months ago
Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure ...
M. M. Hassan Mahmud
PAMI
2007
186views more  PAMI 2007»
13 years 4 months ago
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...
Jesse Hoey, James J. Little
IS
2008
13 years 4 months ago
From conceptual models to schemata: An object-process-based data warehouse construction method
Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's con...
Dov Dori, Roman Feldman, Arnon Sturm
ICTAI
1996
IEEE
13 years 9 months ago
Incremental Markov-Model Planning
This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. T...
Richard Washington
TSMC
2008
117views more  TSMC 2008»
13 years 3 months ago
Discovery of High-Level Behavior From Observation of Human Performance in a Strategic Game
This paper explores the issues faced in creating a sys-4 tem that can learn tactical human behavior merely by observing5 a human perform the behavior in a simulation. More specific...
Brian S. Stensrud, Avelino J. Gonzalez