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Artificial intelligence / Mathematics / Decomposition method / Precondition / Algorithm / Applied mathematics / Automated planning and scheduling / Hierarchical task network / Constraint programming


Learning HTN Method Preconditions and Action Models from Partial Observations
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Document Date: 2009-07-14 20:12:38


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File Size: 501,81 KB

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IEEE Intelligent Systems / Case / HTN / Hierarchical Task Networks / /

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M&A / /

Facility

Building Decomposition Constraints In / Building State Constraints In / Engineering Hong Kong University of Science / /

IndustryTerm

real-world applications / real-world planning applications / eager and lazy learning algorithms / task network / real applications / main learning algorithm / task networks / partial method algorithms / learning algorithm / learning algorithms / /

NaturalFeature

University Clear Water Bay / /

Organization

NEC China Lab / National Science Foundation / Hong Kong University of Science and Technology / Computer Science & Engineering Hong Kong University of Science and Technology Lehigh University Clear Water Bay / /

Person

Chad Hoggb / Derek Hao Hua / Hankz Hankui Zhuoa / Hector Munoz-Avilab / Qiang Yanga / /

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project management assistant / human planner / planner / /

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DC / /

ProvinceOrState

South Carolina / /

PublishedMedium

IEEE Intelligent Systems / /

Technology

corresponding algorithm / learning algorithm / 4.3 Our algorithm / main learning algorithm / existing algorithm / partial method algorithms / 4 Algorithm / eager and lazy learning algorithms / 1 Algorithm / 4.1 Algorithm / /

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http /

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