Abstraction Heuristics for Classical Planning Tasks with Conditional Effects
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
Pozo and Seipp. "Abstraction Heuristics for Classical Planning Tasks with Conditional Effects." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/957Markdown
[Pozo and Seipp. "Abstraction Heuristics for Classical Planning Tasks with Conditional Effects." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/pozo2025ijcai-abstraction/) doi:10.24963/IJCAI.2025/957BibTeX
@inproceedings{pozo2025ijcai-abstraction,
title = {{Abstraction Heuristics for Classical Planning Tasks with Conditional Effects}},
author = {Pozo, Martín and Seipp, Jendrik},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {8608-8616},
doi = {10.24963/IJCAI.2025/957},
url = {https://mlanthology.org/ijcai/2025/pozo2025ijcai-abstraction/}
}