RT Conference Proceedings
T1 On learning control knowledge for a HTN-POP hybrid planner
A1 Fernández, Susana
A1 Aler, Ricardo
A1 Borrajo Millán, Daniel
AB 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.
PB IEEE
SN 0-7803-7508-4
YR 2002
FD 2002-11
LK https://hdl.handle.net/10016/6187
UL https://hdl.handle.net/10016/6187
LA eng
NO Proceeding of: First International Conference on Machine Learning and Cybernetics (ICMLC'02), 4-5 Nov. 2002
DS e-Archivo
RD 19 may. 2024