Constraint satisfaction techniques provide powerful inference algorithms that can prune choices during search. Constraint-based approaches provide a useful complement to heuristic search optimal planners. We develop a constraint-based model for cost-optimal planning that uses global constraints to improve the inference in planning.

The key novelty in our approach is in a transformation of the SAS⁺ input that adds a form of macro-action to fully connect chains of composable operators. This translation leads to the development of a natural dominance constraint on the new problem which we add to our constraint model.

We provide empirical results to show that our planner, Constance, solves more instances than the current best constraint-based planners. We also demonstrate the power of our new dominance constraints in this representation.