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- Institut national de recherche en sciences et technologies du numériqueFR5 papers
- Centre Inria de SaclayFR4 papers
- Thales (France)FR3 papers
- DeKalb County School DistrictUS2 papers
- Laboratoire de Recherche en InformatiqueFR2 papers
- Université Paris-SudFR2 papers
- Centre de Recherche en Informatique de LensFR1 paper
- Office National d'Études et de Recherches AérospatialesFR1 paper
- Princeton UniversityUS1 paper
- Université d'ArtoisFR1 paper
5 papers · 1 filter
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning
Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal +2
Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter…
Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal +2
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 no…
Online Learning for Ground Trajectory Prediction
Areski Hadjaz, Gaétan Marceau, Pierre Savéant +1
This paper presents a model based on an hybrid system to numerically simulate the climbing phase of an aircraft. This model is then used within a trajectory prediction tool. Finall…
Increasing Air Traffic: What is the Problem?
Areski Hadjaz, Gaétan Marceau, Pierre Savéant +1
Nowadays, huge efforts are made to modernize the air traffic management systems to cope with uncertainty, complexity and sub-optimality. An answer is to enhance the information sha…
Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning
Marc Schoenauer, Pierre Savéant, Vincent Vidal
An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Prob…