Learning and Exploiting Progress States in Greedy Best-First Search

Ferber, Patrick and Cohen, Liat and Seipp, Jendrik and Keller, Thomas. (2022) Learning and Exploiting Progress States in Greedy Best-First Search. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. pp. 4740-4746.

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Official URL: https://edoc.unibas.ch/93509/

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Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a single task and only after a solution for the task has been found. We introduce a novel approach that learns a description logic formula characterizing all progress states in a classical planning domain. Using the learned formulas in a GBFS to break ties in favor of progress states often significantly reduces the search effort.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Ferber, Patrick and Cohen, Liat and Keller, Thomas
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:13 Mar 2023 13:38
Deposited On:20 Feb 2023 10:01

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