Using artificial neural networks for constraint satisfaction problem

We address the problem of solving a constraint satisfaction problem (CSP) by treating a constraint logic program (CLP) as a network of constraints. We attempt to show that each computation in a CLP becomes a sequence of linear steps, since the check satisfiability of the system of constraints is applied at each resolution step which is linear in the size of the current constraint problem. Thus, the constraint propagation information is performed at each step during any CLP derivation. The major issues we address here are the identification (using logic interpretation) of constraints that can be added within the program rules to reduce the size of intermediate states and how to use the previous steps of the computation as a guidance for CLP derivations.