Deep Neural Network for Constraint Acquisition through Tailored Loss
  Function
Eduardo Vyhmeister Rocio Paez Gabriel Gonzalez
The significance of learning constraints from data is underscored by its
potential applications in real-world problem-solving. While constraints are
popular for modeling and solving, the approaches to learning constraints from
data remain relatively scarce. Furthermore, the intricate task of modeling
demands expertise and is prone to errors, thus constraint acquisition methods
offer a solution by automating this process through learnt constraints from
examples or behaviours of solutions and non-solutions. This work introduces a
novel approach grounded in Deep Neural Network (DNN) based on Symbolic
Regression that, by setting suitable loss functions, constraints can be
extracted directly from datasets. Using the present approach, direct
formulation of constraints was achieved. Furthermore, given the broad
pre-developed architectures and functionalities of DNN, connections and
extensions with other frameworks could be foreseen.