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    AAAI
    2006
    157views Intelligent Agents» more  AAAI 2006»
    Improving Approximate Value Iteration Using Memories and Predictive State Representations
    13 years 7 months ago
    Improving Approximate Value Iteration Using Memories and Predictive State Representations
    Download  www.aaai.org
    Planning in partially-observable dynamical systems is a challenging problem, and recent developments in point-based techniques such as Perseus significantly improve performance as...
    Michael R. James, Ton Wessling, Nikos A. Vlassis
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    ICML
    2008
    IEEE
    157views Machine Learning» more  ICML 2008»
    Efficiently learning linear-linear exponential family predictive representations of state
    14 years 7 months ago
    Efficiently learning linear-linear exponential family predictive representations of state
    Download  web.mit.edu
    Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
    David Wingate, Satinder P. Singh
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    ATAL
    2010
    Springer
    171views Intelligent Agents» more  ATAL 2010»
    Closing the learning-planning loop with predictive state representations
    13 years 7 months ago
    Closing the learning-planning loop with predictive state representations
    Download  www.cs.cmu.edu
    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
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    NIPS
    2007
    109views Information Technology» more  NIPS 2007»
    Exponential Family Predictive Representations of State
    13 years 7 months ago
    Exponential Family Predictive Representations of State
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    David Wingate, Satinder Singh Baveja
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    RECOMB
    2004
    Springer
    166views Computational Biology» more  RECOMB 2004»
    Learning Regulatory Network Models that Represent Regulator States and Roles
    14 years 6 months ago
    Learning Regulatory Network Models that Represent Regulator States and Roles
    Download  pages.cs.wisc.edu
    Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
    Keith Noto, Mark Craven
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