---
title: "Heuristic Subset Selection in Classical Planning"
authors:
  - "Levi H. S. Lelis"
  - "Santiago Franco"
  - "Marvin Abisrror"
  - "Mike Barley"
  - "Sandra Zilles"
  - "Robert C. Holte"
doi: ""
url: "https://ijcai.org/proceedings/2016/451"
pdf: "https://ijcai.org/proceedings/2016/0451.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3185-3190"
track: ""
keywords:
---

# Heuristic Subset Selection in Classical Planning

Levi H. S. Lelis, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert C. Holte

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3185-3190.

## Abstract

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.