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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!

    • Blum, A. L., and Furst, M. L. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90(1-2):281 - 300.
    • Gerevini, A.; Saetti, A.; and Serina, I. 2003. Planning through stochastic local search and temporal action graphs. J. Artif. Int. Res. 20:239-290.
    • Gerevini, A.; Saetti, A.; and Serina, I. 2008. An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artificial Intelligence 172(8-9):899-944.
    • Gerevini, A.; Saetti, A.; and Vallati, M. 2009. An automatically configurable portfolio-based planner with macroactions: Pbp. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), 350-353.
    • Hoffmann, J., and Edelkamp, S. 2005. The deterministic part of ipc-4: An overview. J. Artif. Int. Res. 24:519-579.
    • Hutter, F.; Babic, D.; Hoos, H. H.; and Hu, A. J. 2007.
    • Boosting verification by automatic tuning of decision procedures. Formal Methods in Computer Aided Design 0:27-34.
    • Hutter, F.; Hoos, H. H.; Leyton-Brown, K.; and Stu¨tzle, T. 2009. Paramils: an automatic algorithm configuration framework. J. Artif. Int. Res. 36:267-306.
    • 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.
    • Nell, C.; Fawcett, C.; Hoos, H. H.; and Leyton-Brown, K. 2011. HAL: A framework for the automated analysis and design of high-performance algorithms. In LION-5, (to appear).
    • 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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