Instance-based parameter tuning for evolutionary AI planning

Brendel Matthias 1 Marc Schoenauer 1, 2, *
* Auteur correspondant
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Learn-and-Optimize (LaO) is a generic surrogate based method for parameter tuning combining learning and optimization. In this paper LaO is used to tune Divide-and- Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate this knowledge to unknown instances in the same domain. Moreover, the learned relation is used as a surrogate-model to accelerate the search for the optimal parameters. It hence becomes possible to solve intra-domain and extra-domain generalization in a single framework. The proposed implementation of LaO uses an Arti cial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimization. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited amount of meaningful features that are available to describe the instances. However, the learned model reaches almost the same performance on the test instances, which means that it is capable of generalization.
Type de document :
Communication dans un congrès
Genetic and Evolutionary Computation Conference, Oct 2011, Dublin, Ireland. 2011
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Soumis le : vendredi 14 octobre 2011 - 11:08:39
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : mardi 13 novembre 2012 - 16:50:42


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Brendel Matthias, Marc Schoenauer. Instance-based parameter tuning for evolutionary AI planning. Genetic and Evolutionary Computation Conference, Oct 2011, Dublin, Ireland. 2011. 〈inria-00632375〉



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