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On learning control knowledge for a HTN-POP hybrid planner

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorFernández, Susana
dc.contributor.authorAler, Ricardo
dc.contributor.authorBorrajo Millán, Daniel
dc.date.accessioned2009-12-21T11:04:18Z
dc.date.available2009-12-21T11:04:18Z
dc.date.issued2002-11
dc.descriptionProceeding of: First International Conference on Machine Learning and Cybernetics (ICMLC'02), 4-5 Nov. 2002
dc.description.abstractIn 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.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationFirst International Conference on Machine Learning and Cybernetics (ICMLC'02), 2002, vol. 4, p. 1899 - 1904
dc.identifier.doi10.1109/ICMLC.2002.1175368
dc.identifier.isbn0-7803-7508-4
dc.identifier.publicationfirstpage1899
dc.identifier.publicationlastpage1904
dc.identifier.publicationtitleFirst International Conference on Machine Learning and Cybernetics (ICMLC'02)
dc.identifier.publicationvolume4
dc.identifier.urihttps://hdl.handle.net/10016/6187
dc.language.isoeng
dc.publisherIEEE
dc.relation.eventdate4-5 Nov. 2002
dc.relation.eventnumber1
dc.relation.eventplaceBeijing (China)
dc.relation.eventtitleInternational Conference on Machine Learning and Cybernetics (ICMLC'02)
dc.relation.publisherversionhttp://dx.doi.org/10.1109/ICMLC.2002.1175368
dc.rights© IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherKnowledge acquisition
dc.subject.otherLearning (artificial intelligence)
dc.subject.otherPlanning (artificial intelligence)
dc.titleOn learning control knowledge for a HTN-POP hybrid planner
dc.typeconference paper*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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