Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling

Résumé

In Constraint Programming, constraints are usually represented as predicates allowing or forbidding combinations of values. However, some algorithms exploit a finer representation: error functions. Their usage comes with a price though: it makes problem modeling significantly harder. Here, we propose a method to automatically learn an error function corresponding to a constraint, given a function deciding if assignments are valid or not. This is, to the best of our knowledge, the first attempt to automatically learn error functions for hard constraints. Our method uses a variant of neural networks we named Interpretable Compositional Networks, allowing us to get interpretable results, unlike regular artificial neural networks. Experiments on 5 different constraints show that our system can learn functions that scale to high dimensions, and can learn fairly good functions over incomplete spaces.
Fichier principal
Vignette du fichier
learning_ef.pdf (281.53 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03286225 , version 1 (14-07-2021)

Identifiants

  • HAL Id : hal-03286225 , version 1

Citer

Florian Richoux, Jean-François Baffier. Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling. Data Science Meets Optimisation (DSO 2021), an International Joint Conference on Artificial Intelligence (​IJCAI-21) workshop, Aug 2021, Montréal, Canada. ⟨hal-03286225⟩
22 Consultations
40 Téléchargements

Partager

Gmail Facebook X LinkedIn More