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
2 LRI - Laboratoire de Recherche en Informatique
3 ONERA - The French Aerospace Lab [Toulouse]
4 Thales Research and Technology [Palaiseau]

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.

Mots clés: AI Planning; Multi-objective optimization

Domaines: Intelligence artificielle [cs.AI]

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⟩