Pierre Savéant

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DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by(More)
All standard Artifical Intelligence (AI) planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Furthermore, and in deep contrast with the single objective case, there exists no benchmark problems on which to test the algorithms for multi-objective(More)
Divide-and-Evolve (DaE) is an original “memeticization” of Evolutionary Computation and Artificial Intelligence Planning. DaE optimizes either the number of actions, or the total cost of actions, or the total makespan, by generating ordered sequences of intermediate goals via artificial evolution. The evolutionary part of DaE is based on the Evolving(More)
Divide-and-Evolve (DAE) is the first evolutionary planner that has entered the biennial International Planning Competition (IPC). Though the overall results were disappointing, a detailed investigation demonstrates that in spite of a harsh time constraint imposed by the competition rules, DAE was able to obtain the best quality results in a number of(More)
<i>Divide-and-Evolve</i> (DaE) is an original "memeticization" of Evolutionary Computation and Artificial Intelligence Planning. However, like any Evolutionary Algorithm, DaE has several parameters that need to be tuned, and the already excellent experimental results demonstrated by DaE on benchmarks from the International Planning Competition, at the level(More)
The sub-optimal DAE planner implements the stochastic approach for domain-independent planning decomposition introduced in (Schoenauer, Savéant, and Vidal 2006; 2007). The purpose of this planner is to optimize the makespan, or the number of actions, by generating ordered sequences of intermediate goals via a process of artificial evolution. For the(More)
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. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm is used to tune five selected parameters, and thus improve the accuracy of the model.(More)
Real-world problems generally involve several antagonistic objectives, like quality and cost for design problems, or makespan and cost for planning problems. The only approaches to multiobjective AI Planning rely on metrics, that can incorporate several objectives in some linear combinations, and metric sensitive planners, that are able to give different(More)
In the design of constraint propagation algorithms, constraint reduction is usually not considered in a same way at the semantic level and at the implementation level. This mismatch prevents from taking advantage in differentiating the general propagation algorithm from those dedicated to specific cases. We suggest to overcome the mismatch problem by(More)