Numeric Planning via Abstraction and Policy Guided Search
Abstract
The real-world application of planning techniques often requires models with numeric fluents. However, these fluents are not directly supported by most planners and heuristics. We describe a family of planning algorithms that takes a numeric planning problem and produces an abstracted representation that can be solved using any classical planner. The resulting abstract plan is generalized into a policy and then used to guide the search in the original numeric domain. We prove that our approach is sound, and we evaluate it on a set of standard benchmarks. We show that it can provide competitive performance when compared to other well-known algorithms for numeric planning, and a significant performance improvement in certain domains.
Cite
Text
Illanes and McIlraith. "Numeric Planning via Abstraction and Policy Guided Search." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/606Markdown
[Illanes and McIlraith. "Numeric Planning via Abstraction and Policy Guided Search." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/illanes2017ijcai-numeric/) doi:10.24963/IJCAI.2017/606BibTeX
@inproceedings{illanes2017ijcai-numeric,
title = {{Numeric Planning via Abstraction and Policy Guided Search}},
author = {Illanes, Leon and McIlraith, Sheila A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4338-4345},
doi = {10.24963/IJCAI.2017/606},
url = {https://mlanthology.org/ijcai/2017/illanes2017ijcai-numeric/}
}