ML Anthology

Symbolic Merge-and-Shrink for Cost-Optimal Planning

Álvaro Torralba Arias de Reyna, Carlos Linares López, Daniel Borrajo
IJCAI 2013 pp. 2394-2400
/ijcai/2013/dereyna2013ijcai-symbolic/

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.

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Text

de Reyna et al. "Symbolic Merge-and-Shrink for Cost-Optimal Planning." International Joint Conference on Artificial Intelligence, 2013.

Markdown

[de Reyna et al. "Symbolic Merge-and-Shrink for Cost-Optimal Planning." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/dereyna2013ijcai-symbolic/)

BibTeX

@inproceedings{dereyna2013ijcai-symbolic,
  title     = {{Symbolic Merge-and-Shrink for Cost-Optimal Planning}},
  author    = {de Reyna, Álvaro Torralba Arias and López, Carlos Linares and Borrajo, Daniel},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {2394-2400},
  url       = {https://mlanthology.org/ijcai/2013/dereyna2013ijcai-symbolic/}
}