ML Anthology

Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract)

Javier Segovia-Aguas, Sergio Jiménez Celorrio, Anders Jonsson
IJCAI 2022 pp. 5334-5338
doi:10.24963/IJCAI.2022/746 /ijcai/2022/segoviaaguas2022ijcai-computing/

Abstract

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions.

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Cite

Text

Segovia-Aguas et al. "Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/746

Markdown

[Segovia-Aguas et al. "Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/segoviaaguas2022ijcai-computing/) doi:10.24963/IJCAI.2022/746

BibTeX

@inproceedings{segoviaaguas2022ijcai-computing,
  title     = {{Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract)}},
  author    = {Segovia-Aguas, Javier and Celorrio, Sergio Jiménez and Jonsson, Anders},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5334-5338},
  doi       = {10.24963/IJCAI.2022/746},
  url       = {https://mlanthology.org/ijcai/2022/segoviaaguas2022ijcai-computing/}
}