How Much Should This Symbol Weigh? A GNN-Advised Clause Selection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367641" target="_blank" >RIV/68407700:21230/23:00367641 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/23:00367641
Result on the web
<a href="https://doi.org/10.29007/5f4r" target="_blank" >https://doi.org/10.29007/5f4r</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.29007/5f4r" target="_blank" >10.29007/5f4r</a>
Alternative languages
Result language
angličtina
Original language name
How Much Should This Symbol Weigh? A GNN-Advised Clause Selection
Original language description
Clause selection plays a crucial role in modern saturation-based automatic theorem provers. A commonly used heuristic suggests prioritizing small clauses, i.e., clauses with few symbol occurrences. More generally, we can give preference to clauses with a low weighted symbol occurrence count, where each symbol’s occurrence count is multiplied by a respective symbol weight. Traditionally, a human domain expert would supply the symbol weights. In this paper, we propose a system based on a graph neural network that learns to predict symbol weights with the aim of improving clause selection for arbitrary first-order logic problems. Our experiments demonstrate that by advising the automatic theorem prover Vampire on the first-order fragment of TPTP using a trained neural network, the prover’s problem solving capability improves by 6.6% compared to uniformly weighting symbols and by 2.1% compared to a goal-directed variant of the uniformly weighting strategy.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
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 24th International Conference on Logic for Programming, Artificial Intelligence and Reasoning (LPAR)
ISBN
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ISSN
2398-7340
e-ISSN
2398-7340
Number of pages
16
Pages from-to
96-111
Publisher name
EasyChair Publications
Place of publication
Manchester
Event location
Manizales
Event date
Jun 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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