An Effective Polynomial Technique for Compiling Conditional Effects Away

Abstract

The paper introduces a novel polynomial compilation technique for the sound and complete removal of conditional effects in classical planning problems. Similar to Nebel's polynomial compilation of conditional effects, our solution also decomposes each action with conditional effects into several simpler actions. However, it does so more effectively by exploiting the actual structure of the given conditional effects. We characterise such a structure using a directed graph and leverage it to significantly reduce the number of additional atoms required, thereby shortening the size of valid plans. Our experimental analysis indicates that this approach enables the effective use of polynomial compilations, offering benefits in terms of modularity and reusability of existing planners. It also demonstrates that a compilation-based approach can be more efficient, either independently or in synergy with state-of-the-art optimal planners that directly support conditional effects.

Cite

Text

Gerevini et al. "An Effective Polynomial Technique for Compiling Conditional Effects Away." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I18.29989

Markdown

[Gerevini et al. "An Effective Polynomial Technique for Compiling Conditional Effects Away." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/gerevini2024aaai-effective/) doi:10.1609/AAAI.V38I18.29989

BibTeX

@inproceedings{gerevini2024aaai-effective,
  title     = {{An Effective Polynomial Technique for Compiling Conditional Effects Away}},
  author    = {Gerevini, Alfonso Emilio and Percassi, Francesco and Scala, Enrico},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {20104-20112},
  doi       = {10.1609/AAAI.V38I18.29989},
  url       = {https://mlanthology.org/aaai/2024/gerevini2024aaai-effective/}
}