Please use this identifier to cite or link to this item: doi:10.22028/D291-29517
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Title: Making Hill-Climbing Great Again through Online Relaxation Refinement and Novelty Pruning
Author(s): Fickert, Maximilian
Editor(s): Bulitko, Vadim
Storandt, Sabine
Language: English
Title: Proceedings of the Eleventh International Symposium on Combinatorial Search
Startpage: 158
Endpage: 162
Publisher/Platform: AAAI Press
Year of Publication: 2018
Title of the Conference: SoCS 2018
Place of the conference: Stockholm, Sweden
Publikation type: Conference Paper
Abstract: Delete relaxation is one of the most successful approaches to classical planning as heuristic search. The precision of these heuristics can be improved by taking some delete information into account, in particular through atomic conjunctions in the hCFF heuristic. It has recently been shown that this heuristic is especially effective when these conjunctions are learned online in a hill-climbing search algorithm. In this work, we devise a natural extension to this approach using novelty pruning, a recently-developed technique that prunes states based on whether they contain facts not seen before in the search. We evaluate our extension on the IPC benchmarks, where it beats LAMA, Mercury, and Dual-BFWS on many domains.
URL of the first publication:
Link to this record: hdl:20.500.11880/28372
ISBN: 978-1-57735-802-2
Date of registration: 25-Nov-2019
Third-party funds sponsorship: DFG Grant "Critically Constrained Planning via Partial Delete Relaxation"
Sponsorship ID: HO 2169/5-1
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Jörg Hoffmann
Collections:Die Universitätsbibliographie

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