Deep Neural Network for Constraint Acquisition through Tailored Loss Function

arXiv:2403.02042v1 Announce Type: new
Abstract: 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 …

abstract acquisition applications arxiv constraints cs.lg cs.sc data deep neural network expertise function loss modeling network neural network popular problem-solving significance through type world