State Encodings for GNN-Based Lifted Planners
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384781" target="_blank" >RIV/68407700:21230/25:00384781 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1609/aaai.v39i25.34853" target="_blank" >https://doi.org/10.1609/aaai.v39i25.34853</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/aaai.v39i25.34853" target="_blank" >10.1609/aaai.v39i25.34853</a>
Alternative languages
Result language
angličtina
Original language name
State Encodings for GNN-Based Lifted Planners
Original language description
The application of graph neural networks (GNNs) to learn heuristic functions in classical planning is gaining traction. Despite the variety of methods proposed in the literature to encode classical planning tasks for GNNs, a comparative study evaluating their relative performances has been lacking. Moreover, some encodings have been assessed solely for their expressiveness rather than practical effectiveness in planning. This paper provides an extensive comparative analysis of existing encodings. Our results indicate that the smallest encoding based on Gaifman graphs, not yet applied in planning, outperforms the rest due to its fast evaluation times and the informativeness of the resulting heuristic. The overall coverage measured on the IPC almost reaches that of the state-of-the-art planner LAMA while exhibiting rather complementary strengths across different domains.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 39th AAAI Conference on Artificial Intelligence
ISBN
978-1-57735-897-8
ISSN
2159-5399
e-ISSN
2374-3468
Number of pages
9
Pages from-to
26525-26533
Publisher name
AAAI Press
Place of publication
Menlo Park
Event location
Philadelphia
Event date
Feb 27, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001477487000037