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Markov processes / Stochastic control / Linear algebra / Partially observable Markov decision process / Principal component analysis / Nonlinear dimensionality reduction / Dimension reduction / Reinforcement learning / Automated planning and scheduling / Statistics / Multivariate statistics / Dynamic programming


Planning under Uncertainty for Reliable Health Care Robotics Nicholas Roy1 , Geoffrey Gordon2 , and Sebastian Thrun3 1 2
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Document Date: 2003-11-30 16:19:00

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File Size: 293,45 KB

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Edmonton / Acapulco / Lake Tahoe / /

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Neural Information Processing Systems / MIT Press / Intelligent Robotic Systems / Neural Processing Systems / Multiagent Systems / Coastal / /

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Mexico / /

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Computer Science Carnegie Mellon University / /

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conversion algorithm / helicopter control using reinforcement learning policy search methods / matrix dot product / accuracy solutions / Policy search algorithms / policy search techniques / policy search / computing / policy search method / /

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National Science Foundation / MIT / School of Computer Science Carnegie Mellon University Pittsburgh / Association for Computational Linguistics / /

Person

Lee / Joelle Pineau / J. Andrew Bagnell / Remi Munos / Michael Montemerlo / Pack Kaelbling / Daniel D. Lee / Andrew Moore / Geoffrey Gordon / Milos Hauskrecht / Sam Roweis / Andrew Ng / Craig Boutilier / Lawrence Saul / Klaus Obermayer / Geoff Gordon / Vandana Verma / Sebastian Thrun / Jeff Schneider / Matthew Rosencrantz / Michael Jordan / Anthony R. Cassandra / Nicholas Roy / Pascal Poupart / H. Sebastian Seung / J. B. Tenenbaum / V / James A. Kurien / /

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Position

fully-observable planner / representative / decision-theoretic model for planning successfully with beliefs / planner / controller / /

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Alberta / Pennsylvania / California / /

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Computational Linguistics / Journal of Artificial Intelligence Research / /

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laser / artificial intelligence / Policy search algorithms / POMDP algorithms / Navigation algorithm / conversion algorithm / Planning algorithms / Machine Learning / /

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