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All standard AI planners to-date can only handle a single objective, and theonly way for them to take into account multiple objectives is by aggregation ofthe objectives. Furthermore, and in deep contrast with the single objectivecase, there exists no benchmark problems on which to test the algorithms formulti-objective planning. Divide and Evolve (DAE) is an evolutionary plannerthat won the (single-objective) deterministic temporal satisficing track in thelast International Planning Competition. Even though it uses intensively theclassical (and hence single-objective) planner YAHSP, it is possible to turnDAE-YAHSP into a multi-objective evolutionary planner. A tunable benchmarksuite for multi-objective planning is first proposed, and the performances ofseveral variants of multi-objective DAE-YAHSP are compared on differentinstances of this benchmark, hopefully paving the road to furthermulti-objective competitions in AI planning.

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