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Vallati, Mauro; Fawcett, Chris; Gerevini, Alfonso; Hoos, Holger; Saetti, Alessandro (2011)
Languages: English
Types: Unknown
Subjects: Q1, QA75, QA76
The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure – here, the well-known ParamILS framework – to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain.\ud This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10^17 possible configurations. A recent, large-scale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Hutter, F.; Hoos, H.; and Leyton-Brown, K. 2010. Automated configuration of mixed integer programming solvers. In Lodi, A.; Milano, M.; and Toth, P., eds., Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, volume 6140 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg. 186-202.
    • Hutter, F.; Hoos, H. H.; and Stu¨tzle, T. 2007. Automatic algorithm configuration based on local search. In Proceedings of the 22nd national conference on Artificial intelligence, 1152-1157. AAAI Press.
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    • Yoon, S.; Fern, A.; and Givan, R. 2008. Learning control knowledge for forward search planning. J. Mach. Learn. Res. 9:683-718.
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