Learning to rank for synthesizing planning heuristics
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Kaelbling_Learning to rank.pdf
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Author(s)
Garrett, Caelan Reed
Kaelbling, Leslie P
Lozano-Perez, Tomas
Date Issued
July 2016
Journal
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Publisher
AAAI Press
Citation
Garrett, Caelan Reed, Leslie Pack Kaelbling, Tomás Lozano-Pérez. "Learning to Rank for Synthesizing Planning Heuristics." Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), 9-15 July, 2016, New York, New York, AAAI Press.
Version
Author's final manuscript
Abstract
We investigate learning heuristics for domainspecific planning. Prior work framed learning a heuristic as an ordinary regression problem. However,
in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner’s performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation. Additionally, we introduce new methods for computing features that capture temporal interactions in an approximate plan. Our experiments on recent International Planning Competition problems show that the RankSVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.ijcai.org/proceedings/2016
