Symbolic Numeric Planning with Patterns
Matteo Cardellini, Enrico Giunchiglia, Marco Maratea
Abstract
In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Pi, a bound n and a pattern --defined as an arbitrary sequence of actions-- we encode the problem of finding a plan for Pi with bound n as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-exists encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems --including the ones which participated in this year's International Planning Competition (IPC)-- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems.
BibTeX
@article{Cardellini_Giunchiglia_Maratea_2024, title={Symbolic Numeric Planning with Patterns}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29985}, DOI={10.1609/aaai.v38i18.29985}, abstractNote={In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Pi, a bound n and a pattern --defined as an arbitrary sequence of actions-- we encode the problem of finding a plan for Pi with bound n as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-exists encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems --including the ones which participated in this year’s International Planning Competition (IPC)-- and we show that our planner Patty has remarkably good comparative performances on this year’s IPC problems.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cardellini, Matteo and Giunchiglia, Enrico and Maratea, Marco}, year={2024}, month={Mar.}, pages={20070-20077} }