Understanding and Improving Local Exploration for GBFS

Authors

  • Fan Xie University of Alberta
  • Martin Müller University of Alberta
  • Robert Holte University of Alberta

DOI:

https://doi.org/10.1609/icaps.v25i1.13704

Keywords:

Artificial Intelligence, Planning, Heuristic Search, GBFS

Abstract

Greedy Best First Search (GBFS) is a powerful algorithm at the heart of many state-of-the-art satisficing planners. The Greedy Best First Search with Local Search (GBFS-LS) algorithm adds exploration using a local GBFS to a global GBFS. This substantially improves performancefor domains that contain large uninformative heuristic regions (UHR), such as plateaus or local minima. This paper analyzes, quantifies and improves the performance of GBFS-LS.Planning problems with a mix of small and large UHRs are shown to be hard for GBFS but easy for GBFS-LS. In three standard IPC planning instances analyzed in detail, adding exploration using local GBFS gives more than three orders of magnitude speedup. As a second contribution, the detailed analysis leads to an improvedGBFS-LS algorithm, which replaces larger-scale local GBFS explorations with a greater number of smaller explorations.

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Published

2015-04-08

How to Cite

Xie, F., Müller, M., & Holte, R. (2015). Understanding and Improving Local Exploration for GBFS. Proceedings of the International Conference on Automated Planning and Scheduling, 25(1), 244-248. https://doi.org/10.1609/icaps.v25i1.13704