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» Observational learning in an uncertain world
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ICRA
2010
IEEE
128views Robotics» more  ICRA 2010»
14 years 8 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
14 years 11 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»
14 years 8 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»
15 years 1 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
14 years 11 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