Efficiently Reasoning with Interval Constraints in Forward Search Planning
Abstract
In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.
Cite
Text
Coles et al. "Efficiently Reasoning with Interval Constraints in Forward Search Planning." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33017562Markdown
[Coles et al. "Efficiently Reasoning with Interval Constraints in Forward Search Planning." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/coles2019aaai-efficiently/) doi:10.1609/AAAI.V33I01.33017562BibTeX
@inproceedings{coles2019aaai-efficiently,
title = {{Efficiently Reasoning with Interval Constraints in Forward Search Planning}},
author = {Coles, Amanda Jane and Coles, Andrew and Martínez, Moisés and Savas, Emre and Delfa, Juan Manuel and de la Rosa, Tomás and E-Martín, Yolanda and Olaya, Angel García},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {7562-7569},
doi = {10.1609/AAAI.V33I01.33017562},
url = {https://mlanthology.org/aaai/2019/coles2019aaai-efficiently/}
}