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Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark

Mostepha Redouane Khouadjia 1 Marc Schoenauer 1, 2 Vincent Vidal 3 Johann Dréo 4 Pierre Savéant 4 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : All standard AI planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Furthermore, and in deep contrast with the single objective case, there exists no benchmark problems on which to test the algorithms for multi-objective planning. Divide and Evolve (DAE) is an evolutionary planner that won the (single-objective) deterministic temporal satisficing track in the last International Planning Competition. Even though it uses intensively the classical (and hence single-objective) planner YAHSP, it is possible to turn DAE-YAHSP into a multi-objective evolutionary planner. A tunable benchmark suite for multi-objective planning is first proposed, and the performances of several variants of multi-objective DAE-YAHSP are compared on different instances of this benchmark, hopefully paving the road to further multi-objective competitions in AI planning.
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Submitted on : Thursday, December 20, 2012 - 7:40:32 PM
Last modification on : Tuesday, October 25, 2022 - 4:20:52 PM
Long-term archiving on: : Thursday, March 21, 2013 - 2:55:10 AM


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  • HAL Id : hal-00750560, version 1
  • ARXIV : 1212.5276


Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal, Johann Dréo, Pierre Savéant. Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark. EMO'13 - 7th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2013, Sheffield, United Kingdom. pp.36-50. ⟨hal-00750560⟩



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