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    IJCAI
    1997
    121views Artificial Intelligence» more  IJCAI 1997»
    Learning to Improve both Efficiency and Quality of Planning
    14 years 11 months ago
    Learning to Improve both Efficiency and Quality of Planning
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    Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has b...
    Tara A. Estlin, Raymond J. Mooney
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    IJCAI
    1997
    98views Artificial Intelligence» more  IJCAI 1997»
    Robust Periodic Planning and Execution for Autonomous Spacecraft
    14 years 11 months ago
    Robust Periodic Planning and Execution for Autonomous Spacecraft
    Download  www.barneypell.com
    The New Millennium Remote Agent (NMRA) will be the rst on-board AI system to control an actual spacecraft. The spacecraft domain raises a number of challenges for planning and exe...
    Barney Pell, Erann Gat, Ron Keesing, Nicola Muscet...
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    IJCAI
    2003
    168views Artificial Intelligence» more  IJCAI 2003»
    A Logic For Causal Reasoning
    14 years 11 months ago
    A Logic For Causal Reasoning
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    We introduce a logical formalism of irreflexivc causal production relations that possesses both a standard monotonic semantics, and a natural nonmonotonic semantics. The formalism...
    Alexander Bochman
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    IJCAI
    2007
    150views Artificial Intelligence» more  IJCAI 2007»
    Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree
    14 years 11 months ago
    Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree
    Download  www.ijcai.org
    In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a ...
    Claudia Henry, Richard Nock, Frank Nielsen
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    IJCAI
    1997
    158views Artificial Intelligence» more  IJCAI 1997»
    Extracting Propositions from Trained Neural Networks
    14 years 11 months ago
    Extracting Propositions from Trained Neural Networks
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    This paper presents an algorithm for extract­ ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
    Hiroshi Tsukimoto
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