ML Anthology

Reviving Partial Order Planning

XuanLong Nguyen, Subbarao Kambhampati
IJCAI 2001 pp. 459-466
/ijcai/2001/nguyen2001ijcai-reviving/

Abstract

This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners--viz., distance based heuristics, reachability analysis and disjunctive constraint handling--can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP # . Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several "parallel" domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility. 1

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Text

Nguyen and Kambhampati. "Reviving Partial Order Planning." International Joint Conference on Artificial Intelligence, 2001.

Markdown

[Nguyen and Kambhampati. "Reviving Partial Order Planning." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/nguyen2001ijcai-reviving/)

BibTeX

@inproceedings{nguyen2001ijcai-reviving,
  title     = {{Reviving Partial Order Planning}},
  author    = {Nguyen, XuanLong and Kambhampati, Subbarao},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2001},
  pages     = {459-466},
  url       = {https://mlanthology.org/ijcai/2001/nguyen2001ijcai-reviving/}
}