Diverse and additive cartesian abstraction heuristics

Abstract

We have recently shown how counterexample-guided ab-straction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel way of finding cost partitionings for explicitly represented abstraction heuristics. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics on many benchmark domains.