Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
This addresses multi-objective planning problems for AI researchers, but it is incremental as it compares existing methods.
The paper tackles the problem of multi-objective AI planning by comparing aggregation and Pareto-based approaches, finding that the Pareto-based method is more useful on a benchmark set.
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.