Heuristic subset selection in classical planning

In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A* search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically, our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.