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

• URL: http://arxiv.org/abs/2304.14659v1
• Date: Fri, 28 Apr 2023 07:09:23 GMT
• Title: MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front
• Authors: Alexandre Quemy, Marc Schoenauer, Johann Dreo
• Abstract summary: Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. We propose a tunable benchmark generator, together with a dedicated solver that provably computes the true Pareto front of the resulting instances. We show how 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.
• Score: 71.19090689055054
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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