Merge-and-Shrink Task Reformulation for Classical Planning

Torralba, Álvaro and Sievers, Silvan. (2019) Merge-and-Shrink Task Reformulation for Classical Planning. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. pp. 5644-5652.

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Official URL: https://edoc.unibas.ch/74977/

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The performance of domain-independent planning systems heavily depends on how the planning task has been modeled. This makes task reformulation an important tool to get rid of unnecessary complexity and increase the robustness of planners with respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S) framework for task reformulation for optimal and satisficing planning. We prove that the flexibility of the underlying representation makes the M&S reformulation methods more powerful than the counterparts based on the more popular finite-domain representation. We adapt delete-relaxation and M&S heuristics to work on the FTS representation and evaluate the impact of our reformulation.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Sievers, Silvan
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:International Joint Conferences on Artificial Intelligence
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:28 Jan 2020 13:44
Deposited On:27 Jan 2020 12:29

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