Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains

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Show simple item record Fernández, Susana Aler, Ricardo Borrajo Millán, Daniel 2009-11-27T12:23:02Z 2009-11-27T12:23:02Z 2005-09
dc.identifier.bibliographicCitation Applied Artificial Intelligence 2005, vol. 19, n. 8, p. 783-809
dc.identifier.issn 0883-9514
dc.description.abstract The application of AI planning techniques to manufacturing Systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly use das the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be use das sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules.
dc.description.sponsorship This work was partially supported by a grant from the Ministerio de Ciencia y Tecnología through projects TAP1999-0535-C02-02, TIC2001-4936-E, and TIC2002-04146-C05-05.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Taylor & Francis
dc.rights © Taylor & Francis
dc.title Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Informática
dc.identifier.doi 10.1080/08839510490964491
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 783
dc.identifier.publicationissue 8
dc.identifier.publicationlastpage 809
dc.identifier.publicationtitle Applied Artificial Intelligence
dc.identifier.publicationvolume 19
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