CaMeL: Learning Method Preconditions for HTN Planning.
(05/01/2014)
A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system learn the HTN methods incrementally under supervision of an expert. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL's soundness, completeness, and convergence properties. We also report experimental results about its speed of...
Tác giả: Ilghami, O.; Nau, D. S.; Munoz-Avila, H.; Aha, D. W. |
Số trang: 11 |
Lĩnh vực: CNTT |
Năm XB: 2006 |
Loại tài liệu: Khác
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CaMeL: Learning Method Preconditions for HTN Planning. | Số trang: 11
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A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system learn the HTN methods incrementally under supervision of an expert. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL's soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.
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