Computing Domain Abstractions for Optimal Classical Planning with Counterexample-Guided Abstraction Refinement

Kreft, Raphael and Büchner, Clemens and Sievers, Silvan and Helmert, Malte. (2023) Computing Domain Abstractions for Optimal Classical Planning with Counterexample-Guided Abstraction Refinement. In: Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023), 33. pp. 221-226.

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Official URL: https://edoc.unibas.ch/95643/

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Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. A popular method for computing abstractions is the counterexample-guided abstraction refinement (CEGAR) principle, which has successfully been used for projections, which are the abstractions underlying pattern databases, and Cartesian abstractions. While projections are simple and fast to compute, Cartesian abstractions subsume projections and hence allow more finegrained abstractions, however at the expense of efficiency. Domain abstractions are a third class of abstractions between projections and Cartesian abstractions in terms of generality. Yet, to the best of our knowledge, they are only briefly considered in the planning literature but have not been used for computing heuristics yet. We aim to close this gap and compute domain abstractions by using the CEGAR principle. Our empirical results show that domain abstractions compare favorably against projections and Cartesian abstractions.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Büchner, Clemens and Kreft, Raphael and Sievers, Silvan and Helmert, Malte
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:AAAI Press
Series Name:Proceedings of the ... AAAI Conference on Artificial Intelligence
Issue Number:1
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:22 Sep 2023 12:30
Deposited On:19 Sep 2023 08:54

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