Building consistencies for partially defined constraints with decision trees and neural networks

Partially Defined Constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using Machine Learning techniques. Since constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for each constraint projections and then we transform the classifiers into a propagator. The first contribution is a generic meta-technique for classifier improvement showing performances comparable to boosting. The second lies in the ability of using the learned concept in constraint-based decision or optimization problems. We presents results using Decision Trees and Artificial Neural Networks for constraint learning and propagation. It opens a new way of integrating Machine Learning in Decision Support Systems.