Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
  Most real-world Planning problems are multi-objective, trying to minimize
both the makespan of the solution plan, and some cost of the actions involved
in the plan. But most, if not all existing approaches are based on
single-objective planners, and use an aggregation of the objectives to remain
in the single-objective context. Divide and Evolve (DaE) is an evolutionary
planner that won the temporal deterministic satisficing track at the last
International Planning Competitions (IPC). Like all Evolutionary Algorithms
(EA), it can easily be turned into a Pareto-based Multi-Objective EA. It is
however important to validate the resulting algorithm by comparing it with the
aggregation approach: this is the goal of this paper. The comparative
experiments on a recently proposed benchmark set that are reported here
demonstrate the usefulness of going Pareto-based in AI Planning.
