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

MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front