Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In this work, we propose a tunable benchmark generator, together with a dedicated solver that provably computes the true Pareto front of the resulting instances. First, we prove a proposition allowing us to characterize the optimal plans for a constrained version of the problem, and then show how to reduce the general problem to the constrained one. Second, we provide a constructive way to find all the Pareto-optimal plans and discuss the complexity of the algorithm. We provide an implementation that allows the solver to handle realistic instances in a reasonable time. Finally, as a practical demonstration, we used this solver to find all Pareto-optimal plans between the two largest airports in the world, considering the routes between the 50 largest airports, spherical distances between airports and a made-up risk.

翻译：多目标AI规划缺乏展示已知帕累托前沿的基准。在本文中，我们提出了一种可调基准生成器，并配备专用求解器，该求解器可证明地计算出所得实例的真实帕累托前沿。首先，我们证明了一个命题，该命题使我们能够描述该问题约束版本的最优规划，随后展示了如何将一般问题简化为约束版本。其次，我们提供了一种构造性方法以找出所有帕累托最优规划，并讨论了该算法的复杂度。我们提供了一种实现，使求解器能够在合理时间内处理实际规模的实例。最后，作为实践演示，我们利用该求解器，考虑全球50个最大机场之间的航线、机场间的球面距离以及虚构风险因素，找出了世界上两个最大机场之间的所有帕累托最优规划。