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• W4316173691 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." @default.
• W4316173691 created "2023-01-15" @default.
• W4316173691 creator A5048185370 @default.
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• W4316173691 date "2023-02-20" @default.
• W4316173691 modified "2023-10-06" @default.
• W4316173691 title "Automatic error function learning with interpretable compositional networks" @default.
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• W4316173691 doi "https://doi.org/10.1007/s10472-022-09829-8" @default.
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