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NIPS
2008
15 years 7 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
ATAL
2010
Springer
15 years 7 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 6 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
AVI
2010
15 years 3 months ago
An infrastructure for creating graphical indicators of the learner profile by mashing up different sources
The procedures to collect information about users are well known in computer science till long time. They range from getting explicit information from users, required in order to ...
Luca Mazzola, Riccardo Mazza
199
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CHI
2011
ACM
14 years 9 months ago
Modeling users of intelligent systems
While many devices today increasingly have the ability to predict human activities, it is still difficult to build accurate personalized machine learning models. As users today wi...
Stephanie Rosenthal