Learning Action Strategies for Planning Domains using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Levine:evowks03,
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author = "John Levine and David Humphreys",
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title = "Learning Action Strategies for Planning Domains using
Genetic Programming",
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booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
Evo{MUSART}, Evo{ROB}, Evo{STIM}",
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year = "2003",
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editor = "G{\"u}nther R. Raidl and Stefano Cagnoni and
Juan Jes\'us Romero Cardalda and David W. Corne and
Jens Gottlieb and Agn\`es Guillot and Emma Hart and
Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and
Martin Middendorf",
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volume = "2611",
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series = "LNCS",
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pages = "684--695",
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address = "University of Essex, England, UK",
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publisher_address = "Berlin",
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month = "14-16 " # apr,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications",
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isbn13 = "978-3-540-00976-4",
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URL = "http://www.cis.strath.ac.uk/~johnl/papers/levine-evostim03.pdf",
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URL = "http://citeseer.ist.psu.edu/569259.html",
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DOI = "doi:10.1007/3-540-36605-9_62",
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size = "13 pages",
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abstract = "There are many different approaches to solving
planning problems, one of which is the use of domain
specific control knowledge to help guide a domain
independent search algorithm. This paper presents
L2Plan which represents this control knowledge as an
ordered set of control rules, called a policy, and
learns using genetic programming. The genetic program's
crossover and mutation operators are augmented by a
simple local search. L2Plan was tested on both the
blocks world and briefcase domains. In both domains,
L2Plan was able to produce policies that solved all the
test problems and which outperformed the hand-coded
policies written by the authors.",
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notes = "EvoWorkshops2003",
- }
Genetic Programming entries for
John Levine
David Humphreys
Citations