Flexible abstraction heuristics for optimal sequential planning

We describe an approach to deriving consistent heuristics for automated planning, based on explicit search in abstract state spaces. The key to managing complexity is interleaving composition of abstractions over different sets of state variables with abstraction of the partial composites. The approach is very general and can be instantiated in many different ways by following different abstraction strategies. In particular, the technique subsumes planning with pattern databases as a special case. Moreover, with suitable abstraction strategies it is possible to derive perfect heuristics in a number of classical benchmark domains, thus allowing their optimal solution in polynomial time. To evaluate the practical usefulness of the approach, we perform empirical experiments with one particular abstraction strategy. Our results show that the approach is competitive with the state of the art.