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On learning control knowledge for a HTN-POP hybrid planner

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URI: https://hdl.handle.net/10016/6187
ISBN: 0-7803-7508-4
DOI: 10.1109/ICMLC.2002.1175368
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learning_aler_MLC_2002.pdf (295.68 KB)
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2002-11
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IEEE
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Abstract
In this paper we present a learning method that is able to automatically acquire control knowledge for a hybrid HTN-POP planner called HYBIS. HYBIS decomposes a problem in subproblems using either a default method or a user-defined decomposition method. Then, at each level of abstraction, it generates a plan at that level using a POCL (Partial Order Causal Link) planning technique. Our learning approach builds on HAMLET, a system that learns control knowledge for a total order non-linear planner (PRODIGY4.0). In this paper, we focus on the operator selection problem for the POP component of HYBIS, which is very important for efficiency purposes.
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Proceeding of: First International Conference on Machine Learning and Cybernetics (ICMLC'02), 4-5 Nov. 2002
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Knowledge acquisition, Learning (artificial intelligence), Planning (artificial intelligence)
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First International Conference on Machine Learning and Cybernetics (ICMLC'02), 2002, vol. 4, p. 1899 - 1904
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