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arXiv · 2002.09811

Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling

Florian Richoux·Jean-François Baffier
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Abstract

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.

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BibTeXRIS

Florian Richoux, Jean-François Baffier. 2020-02-23. Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling. https://doi.org/10.1007/s10472-022-09829-8

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