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ATAL
2010
Springer
13 years 11 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
ICRA
2010
IEEE
142views Robotics» more  ICRA 2010»
13 years 8 months ago
Learning and planning high-dimensional physical trajectories via structured Lagrangians
— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to s...
Paul Vernaza, Daniel D. Lee, Seung-Joon Yi
ABIALS
2008
Springer
13 years 11 months ago
Anticipatory Driving for a Robot-Car Based on Supervised Learning
Abstract. Prediction and Planning are essential elements of successful human driving, making them equally important for autonomously driving systems. Many approaches achieve planni...
Irene Markelic, Tomas Kulvicius, Minija Tamosiunai...
IJAMC
2008
93views more  IJAMC 2008»
13 years 9 months ago
Meaningful access: policy, management and orchestration
: Access management for learning communities requires a unified theory, sustaining the implementation of instructional policies, for `social networks'. The management method w...
Ioan Rosca, Val Rosca
ATAL
2010
Springer
13 years 11 months ago
Planning against fictitious players in repeated normal form games
Planning how to interact against bounded memory and unbounded memory learning opponents needs different treatment. Thus far, however, work in this area has shown how to design pla...
Enrique Munoz de Cote, Nicholas R. Jennings