Sciweavers

2082 search results - page 34 / 417
» Using model knowledge for learning inverse dynamics
Sort
View
WSC
1997
13 years 10 months ago
Interactive Strategies for Developing Intuitive Knowledge as Basis for Simulation Modeling Education
This paper investigates theoretically based instructional approaches for organizational training, education and knowledge acquisition for simulation modeling. It proposes differen...
Tajudeen A. Atolagbe, Vlatka Hlupic, Simon J. E. T...
TSP
2010
13 years 3 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
KI
2002
Springer
13 years 8 months ago
Acquisition of Landmark Knowledge from Static and Dynamic Presentation of Route Maps
This contribution reports on ongoing collaborative research at the University of Stanford, Department of Psychology, and the University of Hamburg, Department for Informatics. Ext...
Paul U. Lee, Heike Tappe, Alexander Klippel
ICML
2008
IEEE
14 years 9 months ago
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
Finale Doshi, Joelle Pineau, Nicholas Roy
NIPS
1997
13 years 10 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung