Forward-tracking: a technique for searching beyond failure

In many applications, such as decision support, negotiation, planning, scheduling, etc., one needs to express requirements that can only be partially satisfied. In order to express such requirements, we propose a technique called forward-tracking. Intuitively, forward-tracking is a kind of dual of chronological back-tracking: if a program globally fails to find a solution, then a new execution is started from a program point and a state ´forward´ in the computation tree. This search technique is applied to constraint logic programming, obtaining a powerful extension that preserves all the useful properties of the original scheme. We report on the successful practical application of forward-tracking to the evolutionary training of(constrained) neural networks.