Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint)
Abstract
In planning tasks, conditional effects model action outcomes that depend on the current state of the world. Conditional effects are a crucial modeling feature since compiling them away can cause an exponential growth in task size. However, only a few admissible heuristics support them. To add abstraction heuristics to this set, we show how to compute projections, Cartesian abstractions and merge-and-shrink abstractions for tasks with conditional effects. Our experiments show that these heuristics are competitive with, and often surpass, the state-of-the-art for conditional-effect tasks.
Cite
Text
Srivastava. "Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint)." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/957Markdown
[Srivastava. "Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint)." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/srivastava2024ijcai-hierarchical/) doi:10.24963/ijcai.2024/957BibTeX
@inproceedings{srivastava2024ijcai-hierarchical,
title = {{Hierarchical Decompositions and Termination Analysis for Generalized Planning (Abstract Reprint)}},
author = {Srivastava, Siddharth},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {8485},
doi = {10.24963/ijcai.2024/957},
url = {https://mlanthology.org/ijcai/2024/srivastava2024ijcai-hierarchical/}
}