Using genetic programming to learn and improve control knowledge

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dc.contributor.author Aler, Ricardo
dc.contributor.author Borrajo Millán, Daniel
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2009-04-14T12:41:24Z
dc.date.available 2009-04-14T12:41:24Z
dc.date.issued 2002-10
dc.identifier.bibliographicCitation Artificial Intelligence, 2002, vol. 141, n. 1-2, p. 29–56
dc.identifier.issn 0004-3702
dc.identifier.uri http://hdl.handle.net/10016/3937
dc.description.abstract The purpose of this article is to present a multi-strategy approach to learn heuristics for planning. This multi-strategy system, called HAMLET-EVOCK, combines a learning algorithm specialized in planning (HAMLET) and a genetic programming (GP) based system (EVOCK: Evolution of Control Knowledge). Both systems are able to learn heuristics for planning on their own, but both of them have weaknesses. Based on previous experience and some experiments performed in this article, it is hypothesized that HAMLET handicaps are due to its example-driven operators and not having a way to evaluate the usefulness of its control knowledge. It is also hypothesized that even if HAMLET control knowledge is sometimes incorrect, it might be easily correctable. For this purpose, a GP-based stage is added, because of its complementary biases: GP genetic operators are not example-driven and it can use a fitness function to evaluate control knowledge. HAMLET and EVOCK are combined by seeding EVOCK initial population with HAMLET control knowledge. It is also useful for EVOCK to start from a knowledge-rich population instead of a random one. By adding the GP stage to HAMLET, the number of solved problems increases from 58% to 85% in the blocks world and from 50% to 87% in the logistics domain (0% to 38% and 0% to 42% for the hardest instances of problems considered).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier
dc.subject.other Speedup learning
dc.subject.other Genetic programming
dc.subject.other Multi-strategy learning
dc.subject.other Planning
dc.title Using genetic programming to learn and improve control knowledge
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1016/S0004-3702(02)00246-1
dc.subject.eciencia Informática
dc.identifier.doi 10.1016/S0004-3702(02)00246-1
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 29
dc.identifier.publicationissue 1-2
dc.identifier.publicationtitle Artificial Intelligence
dc.identifier.publicationvolume 141
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