@article{Rovner_Sievers_Helmert_2021, title={Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning}, volume={29}, url={https://ojs.aaai.org/index.php/ICAPS/article/view/3499}, DOI={10.1609/icaps.v29i1.3499}, abstractNote={<p>We describe a new algorithm for generating pattern collections for pattern database heuristics in optimal classical planning. The algorithm uses the counterexample-guided abstraction refinement (CEGAR) principle to guide the pattern selection process. Our experimental evaluation shows that a single run of the CEGAR algorithm can compute informative pattern collections in a fairly short time. Using multiple CEGAR algorithm runs, we can compute much larger pattern collections, still in shorter time than existing approaches, which leads to a planner that outperforms the state-of-the-art pattern selection methods by a significant margin.</p>}, number={1}, journal={Proceedings of the International Conference on Automated Planning and Scheduling}, author={Rovner, Alexander and Sievers, Silvan and Helmert, Malte}, year={2021}, month={May}, pages={362-367} }