Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning

Rovner, Alexander and Sievers, Silvan and Helmert, Malte. (2019) Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning. In: Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019), 29. pp. 362-367.

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Official URL: https://edoc.unibas.ch/71594/

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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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Sievers, Silvan and Rovner, Alexander and Helmert, Malte
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:AAAI Press
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:21 Aug 2019 14:54
Deposited On:20 Aug 2019 08:08

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