@inproceedings{c6fb3c518e9a446c929e16ec1b559f41,
title = "Heuristics for planning with SAT",
abstract = "Generic SAT solvers have been very successful in solving hard combinatorial problems in various application areas, including AI planning. There is potential for improved performance by making the SAT solving process more application-specific. In this paper we propose a variable selection strategy for AI planning. The strategy is based on generic principles about properties of plans, and its performance with standard planning benchmarks often substantially improves on generic variable selection heuristics used in SAT solving, such as the VSIDS strategy. These improvements lift the efficiency of SAT based planning to the same level as best planners that use other search methods.",
author = "Jussi Rintanen",
year = "2010",
doi = "10.1007/978-3-642-15396-9\_34",
language = "English",
isbn = "364215395X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "414--428",
booktitle = "Principles and Practice of Constraint Programming, CP 2010 - 16th International Conference, Proceedings",
address = "Germany",
note = "16th International Conference on Principles and Practice of Constraint Programming, CP 2010 ; Conference date: 06-09-2010 Through 10-09-2010",
}