Heuristic subset selection in classical planning
Levi H. S. Lelis, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert C. Holte · ResearchSpace (University of Auckland) · 2016
In this paper we present greedy methods for select- ing 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. Specif- ically, our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our meth- ods can outperform state-of-the-art planners for de- terministic optimal planning.