Symbolic merge-and-shrink for cost-optimal planning

Authors:
Álvaro Torralba;Carlos Linares López;Daniel Borrajo
Affiliations:
Planning and Learning Group, Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Planning and Learning Group, Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Planning and Learning Group, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the best of them for most domains.