ML Anthology

Getting the Most Out of Pattern Databases for Classical Planning

Florian Pommerening, Gabriele Röger, Malte Helmert
IJCAI 2013 pp. 2357-2364
/ijcai/2013/pommerening2013ijcai-getting/

Abstract

The iPDB procedure by Haslum et al. is the state-of-the-art method for computing additive abstraction heuristics for domain-independent planning. It performs a hill-climbing search in the space of pattern collections, combining information from multiple patterns in the so-called canonical heuristic. We show how stronger heuristic estimates can be obtained through linear programming. An experimental evaluation demonstrates the strength of the new technique on the IPC benchmark suite.

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Cite

Text

Pommerening et al. "Getting the Most Out of Pattern Databases for Classical Planning." International Joint Conference on Artificial Intelligence, 2013.

Markdown

[Pommerening et al. "Getting the Most Out of Pattern Databases for Classical Planning." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/pommerening2013ijcai-getting/)

BibTeX

@inproceedings{pommerening2013ijcai-getting,
  title     = {{Getting the Most Out of Pattern Databases for Classical Planning}},
  author    = {Pommerening, Florian and Röger, Gabriele and Helmert, Malte},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {2357-2364},
  url       = {https://mlanthology.org/ijcai/2013/pommerening2013ijcai-getting/}
}