Constraint Based Planning with Composable Substate Graphs.

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.