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    AIPS
    2000
    135views Artificial Intelligence» more  AIPS 2000»
    Dynamic Programming for POMDPs Using a Factored State Representation
    14 years 6 days ago
    Dynamic Programming for POMDPs Using a Factored State Representation
    Download  www.aaai.org
    Eric A. Hansen, Zhengzhu Feng
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    AAAI
    1996
    197views Intelligent Agents» more  AAAI 1996»
    Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
    14 years 5 days ago
    Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
    Download  people.cs.ubc.ca
    : Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
    Craig Boutilier, David Poole
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    AAAI
    2010
    185views Intelligent Agents» more  AAAI 2010»
    Symbolic Dynamic Programming for First-order POMDPs
    14 years 9 days ago
    Symbolic Dynamic Programming for First-order POMDPs
    Download  www-kd.iai.uni-bonn.de
    Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
    Scott Sanner, Kristian Kersting
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    IJRR
    2010
    162views more  IJRR 2010»
    Planning under Uncertainty for Robotic Tasks with Mixed Observability
    13 years 9 months ago
    Planning under Uncertainty for Robotic Tasks with Mixed Observability
    Download  motion.comp.nus.edu.sg
    Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
    Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
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    NIPS
    2003
    145views Information Technology» more  NIPS 2003»
    A Nonlinear Predictive State Representation
    14 years 6 days ago
    A Nonlinear Predictive State Representation
    Download  books.nips.cc
    Predictive state representations (PSRs) use predictions of a set of tests to represent the state of controlled dynamical systems. One reason why this representation is exciting as...
    Matthew R. Rudary, Satinder P. Singh
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