Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms & More Powerful Properties
Abstract
Justifying a plan to a user requires answering questions about the space of possible plans. Recent work introduced a framework for doing so via plan-property dependencies, where plan properties p are Boolean functions on plans, and p entails q if all plans that satisfy p also satisfy q. We extend this work in two ways. First, we introduce new algorithms for computing plan-property dependencies, leveraging symbolic search and devising pruning methods for this purpose. Second, while the properties p were previously limited to goal facts and so-called action-set (AS) properties, here we extend them to LTL. Our new algorithms vastly outperform the previous ones, and our methods for LTL cause little overhead on AS properties.
Cite
Text
Eifler et al. "Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms & More Powerful Properties." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/566Markdown
[Eifler et al. "Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms & More Powerful Properties." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/eifler2020ijcai-plan/) doi:10.24963/IJCAI.2020/566BibTeX
@inproceedings{eifler2020ijcai-plan,
title = {{Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms & More Powerful Properties}},
author = {Eifler, Rebecca and Steinmetz, Marcel and Torralba, Álvaro and Hoffmann, Jörg},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4091-4097},
doi = {10.24963/IJCAI.2020/566},
url = {https://mlanthology.org/ijcai/2020/eifler2020ijcai-plan/}
}