Add to ORKGInternational audienceMulti-objective AI planning suffers from a lack of bench-marks with known Pareto Fronts. A tunable benchmark generator is pro-posed, together with a specific solver that provably computes the true Pareto Front of the resulting instances. A wide range of Pareto Front shapes of various difficulty can be obtained by varying the parameters of the generator. The experimental performances of an actual implemen-tation of the exact solver are demonstrated, and some large instances with remarkable Pareto Front shapes are proposed, that will hopefully become standard benchmarks of the AI planning domain
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping fun...
different approximation methods are utilized in the field of optimization. Here we consider two type...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem i...
International audienceMulti-objective AI planning suffers from a lack of bench-marks with known Pare...
International audienceA method to generate various size tunable benchmarks for multi-objective AI pl...
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In thi...
International audienceReal-world problems generally involve several antagonistic objectives, like qu...
International audienceMost real-world Planning problems are multi-objective, trying to minimize both...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique...
International audienceAll standard AI planners to-date can only handle a single objective, and the o...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique objecti...
In multi-objective optimization problems, expensive high-fidelity simulations are commonly replaced ...
An approach to constructing a Pareto front approximation to computationally expensive multiobjective...
This paper proposes a new benchmark for multi-objective optimization. A solution is furnished which ...
© 2017 Solving a multi-objective optimization problem yields an infinite set of points in which no o...
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping fun...
different approximation methods are utilized in the field of optimization. Here we consider two type...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem i...
International audienceMulti-objective AI planning suffers from a lack of bench-marks with known Pare...
International audienceA method to generate various size tunable benchmarks for multi-objective AI pl...
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In thi...
International audienceReal-world problems generally involve several antagonistic objectives, like qu...
International audienceMost real-world Planning problems are multi-objective, trying to minimize both...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique...
International audienceAll standard AI planners to-date can only handle a single objective, and the o...
International audienceMultidisciplinary Design Optimization (MDO) problems can have a unique objecti...
In multi-objective optimization problems, expensive high-fidelity simulations are commonly replaced ...
An approach to constructing a Pareto front approximation to computationally expensive multiobjective...
This paper proposes a new benchmark for multi-objective optimization. A solution is furnished which ...
© 2017 Solving a multi-objective optimization problem yields an infinite set of points in which no o...
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping fun...
different approximation methods are utilized in the field of optimization. Here we consider two type...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem i...