Although the deep affinity between Graphplan's backward search, and the process of solving constraint satisfaction problems has been noted earlier, these relations have hither-to been primarily used to adapt CSP search techniques into the backward search phase of Graphplan. This paper describes GP-CSP, a system that does planning by automatically converting Graphplan's planning graph into a CSP encoding, and solving the CSP encoding using standard CSP solvers. Our comprehensive empirical...
%0 Journal Article
%1 Do01
%A Do, Minh B.
%A Kambhampati, Subbarao
%D 2001
%J Artificial Intelligence
%K constraint\_satisfaction, csp, planning
%N 2
%P 151--182
%T Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5029
%V 132
%X Although the deep affinity between Graphplan's backward search, and the process of solving constraint satisfaction problems has been noted earlier, these relations have hither-to been primarily used to adapt CSP search techniques into the backward search phase of Graphplan. This paper describes GP-CSP, a system that does planning by automatically converting Graphplan's planning graph into a CSP encoding, and solving the CSP encoding using standard CSP solvers. Our comprehensive empirical...
@article{Do01,
abstract = {{Although the deep affinity between Graphplan's backward search, and the process of solving constraint satisfaction problems has been noted earlier, these relations have hither-to been primarily used to adapt CSP search techniques into the backward search phase of Graphplan. This paper describes GP-CSP, a system that does planning by automatically converting Graphplan's planning graph into a CSP encoding, and solving the CSP encoding using standard CSP solvers. Our comprehensive empirical...}},
added-at = {2011-05-04T16:04:17.000+0200},
author = {Do, Minh B. and Kambhampati, Subbarao},
biburl = {https://www.bibsonomy.org/bibtex/24ba791d6c5db095720819f7d25e5367e/baisemain},
citeulike-article-id = {1297522},
citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5029},
interhash = {eef3a1c8b4700b1edeb98c7097e21311},
intrahash = {4ba791d6c5db095720819f7d25e5367e},
journal = {Artificial Intelligence},
keywords = {constraint\_satisfaction, csp, planning},
number = 2,
pages = {151--182},
posted-at = {2007-05-15 17:57:57},
priority = {4},
timestamp = {2011-05-04T16:04:38.000+0200},
title = {{Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.5029},
volume = 132,
year = 2001
}