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