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» Observational learning in an uncertain world
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ICRA
2010
IEEE
128views Robotics» more  ICRA 2010»
13 years 6 months ago
A game-theoretic procedure for learning hierarchically structured strategies
— This paper addresses the problem of acquiring a hierarchically structured robotic skill in a nonstationary environment. This is achieved through a combination of learning primi...
Benjamin Rosman, Subramanian Ramamoorthy
CLIMA
2004
13 years 8 months ago
The Apriori Stochastic Dependency Detection (ASDD) Algorithm for Learning Stochastic Logic Rules
Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environm...
Christopher Child, Kostas Stathis
IJRR
2010
107views more  IJRR 2010»
13 years 5 months ago
Non-parametric Learning to Aid Path Planning over Slopes
— This paper addresses the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric l...
Sisir Karumanchi, Thomas Allen, Tim Bailey, Steve ...
VW
1998
Springer
174views Virtual Reality» more  VW 1998»
13 years 11 months ago
ALife Meets Web: Lessons Learned
Arti cial life might come to play important roles for the World Wide Web, both as a source of new algorithmic paradigms and as a source of inspiration for its future development. N...
Luigi Pagliarini, Ariel Dolan, Filippo Menczer, He...
IJCAI
2007
13 years 8 months ago
Utile Distinctions for Relational Reinforcement Learning
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
William Dabney, Amy McGovern