%0 Conference Paper %1 Levine:evowks03 %A Levine, John %A Humphreys, David %B Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM %C University of Essex, England, UK %D 2003 %E Raidl, Günther R. %E Cagnoni, Stefano %E Cardalda, Juan Jesús Romero %E Corne, David W. %E Gottlieb, Jens %E Guillot, Agnès %E Hart, Emma %E Johnson, Colin G. %E Marchiori, Elena %E Meyer, Jean-Arcady %E Middendorf, Martin %I Springer-Verlag %K algorithms, applications computation, evolutionary genetic programming, %P 684--695 %T Learning Action Strategies for Planning Domains using Genetic Programming %U http://citeseer.ist.psu.edu/569259.html %V 2611 %X 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.

@inproceedings{Levine:evowks03, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {University of Essex, England, UK}, author = {Levine, John and Humphreys, David}, biburl = {https://www.bibsonomy.org/bibtex/2fb3bf9972b20d6be2d047280c15db4e9/brazovayeye}, booktitle = {Applications of Evolutionary Computing, EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP}, Evo{MUSART}, Evo{ROB}, Evo{STIM}}, editor = {Raidl, G{\"u}nther R. and Cagnoni, Stefano and Cardalda, Juan Jes\'us Romero and Corne, David W. and Gottlieb, Jens and Guillot, Agn\`es and Hart, Emma and Johnson, Colin G. and Marchiori, Elena and Meyer, Jean-Arcady and Middendorf, Martin}, interhash = {95b7a5e3b50fa8a5d871043ecc867c13}, intrahash = {fb3bf9972b20d6be2d047280c15db4e9}, keywords = {algorithms, applications computation, evolutionary genetic programming,}, month = {14-16 April}, notes = {EvoWorkshops2003}, organisation = {EvoNet}, pages = {684--695}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, size = {13 pages}, timestamp = {2008-06-19T17:45:28.000+0200}, title = {Learning Action Strategies for Planning Domains using Genetic Programming}, url = {http://citeseer.ist.psu.edu/569259.html}, volume = 2611, year = 2003 }