Integrating planning and learning: the prodigy architecture

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dc.contributor.author Veloso, Manuela
dc.contributor.author Carbonel, Jaime
dc.contributor.author Pérez, Alicia
dc.contributor.author Borrajo, Daniel
dc.contributor.author Fink, Eugene
dc.contributor.author Blythe, Jim
dc.date.accessioned 2010-02-05T12:20:45Z
dc.date.available 2010-02-05T12:20:45Z
dc.date.issued 1995
dc.identifier.bibliographicCitation Journal of experimental and theoretical Artificial intelligence, 1995, vol. 7, no. 1, p. 81-120
dc.identifier.issn 1362-3079 (online)
dc.identifier.issn 0952-813X (print)
dc.identifier.uri http://hdl.handle.net/10016/6770
dc.description.abstract Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner’s decision points and integration in PRODIGY is achieved via mutually interpretable knowledge structures. This article describes the PRODIGY planner, briefly reports on several learning modules developed earlier along the project, and present sin more detail two recently explored methods to learn to generate plans of better quality. We introduce the techniques, illustrate them with comprehensive examples, and show preliminary empirical results. The article also includes a retrospective discussion of the characteristics of the overall PRODIGY architecture and discusses their evolution with in the goal of the project of building a large and robust integrated planning and learning system.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Taylor & Francis
dc.rights © Taylor & Francis
dc.title Integrating planning and learning: the prodigy architecture
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1080/09528139508953801
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 81
dc.identifier.publicationlastpage 120
dc.identifier.publicationtitle Journal of experimental and theoretical Artificial intelligence
dc.identifier.publicationvolume 7
dc.identifier.uxxi AR/0000010650
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