Expressiveness of Graph Neural Networks in Planning Domains
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00377976" target="_blank" >RIV/68407700:21230/24:00377976 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1609/icaps.v34i1.31486" target="_blank" >https://doi.org/10.1609/icaps.v34i1.31486</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1609/icaps.v34i1.31486" target="_blank" >10.1609/icaps.v34i1.31486</a>
Alternative languages
Result language
angličtina
Original language name
Expressiveness of Graph Neural Networks in Planning Domains
Original language description
Graph Neural Networks (GNNs) have become the standard method of choice for learning with structured data, demonstrating particular promise in classical planning. Their inherent invariance under symmetries of the input graphs endows them with superior generalization capabilities, compared to their symmetry-oblivious counterparts. However, this comes at the cost of limited expressive power. Particularly, GNNs cannot distinguish between graphs that satisfy identical sentences of C2 logic. To leverage GNNs for learning policies in PDDL domains, one needs to encode the contextual representation of the planning states as graphs. The expressiveness of this encoding, coupled with a specific GNN architecture, then hinges on the absence of indistinguishable states necessitating distinct actions. This paper provides a comprehensive theoretical and statistical exploration of such situations in PDDL domains across diverse natural encoding schemes and GNN models.
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
2024
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 Thirty-Fourth International Conference on Automated Planning and Scheduling
ISBN
978-1-57735-889-3
ISSN
2334-0835
e-ISSN
2334-0843
Number of pages
9
Pages from-to
281-289
Publisher name
AAAI Press
Place of publication
Menlo Park, California
Event location
Banff
Event date
Jun 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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