Learning Action Strategies for Planning Domains using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Levine:evowks03,

• author = "John Levine and David Humphreys",
• title = "Learning Action Strategies for Planning Domains using Genetic Programming",
• booktitle = "Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}",
• year = "2003",
• 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",
• volume = "2611",
• series = "LNCS",
• pages = "684--695",
• address = "University of Essex, England, UK",
• publisher_address = "Berlin",
• month = "14-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, evolutionary computation, applications",
• isbn13 = "978-3-540-00976-4",
• URL = "http://www.cis.strath.ac.uk/~johnl/papers/levine-evostim03.pdf",
• URL = "http://citeseer.ist.psu.edu/569259.html",
• DOI = "doi:10.1007/3-540-36605-9_62",
• size = "13 pages",
• 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.",
• notes = "EvoWorkshops2003",

}

Genetic Programming entries for John Levine David Humphreys

Citations