AAAI Publications, Twenty-Eighth International Conference on Automated Planning and Scheduling

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MS-Lite: A Lightweight, Complementary Merge-and-Shrink Method
Gaojian Fan, Robert Holte, Martin Mueller

Last modified: 2018-06-15

Abstract


Merge-and-shrink is a general framework for creating abstraction heuristics. In this paper we present two new variations of merge-and-shrink: MS-lite and DM-HQ. MS-lite is an extremely fast merge-and-shrink that maintains only the smallest abstractions that preserve local heuristic information. MS-lite has complementary strength over other merge-and-shrink methods due to its efficiency. In addition, we show that MS-lite has little dependence on merging strategies and its eager shrinking strategy can lead to better heuristics for some planning tasks. DM-HQ features a merging criterion that utilizes information about heuristic quality to make the merging decisions. Our experiments show that combining DM-HQ and MS-lite dramatically outperforms the current state-of-the-art merge-and-shrink method by solving 75 more tasks on an International Planning Competition (IPC) benchmark set of 1499 tasks.

Keywords


merge-and-shrink; optimal planning; classical planning; heuristic search; merging strategy; international planning competition;

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