Predicting the satisfiability of Boolean formulas by incorporating gated recurrent unit (GRU) in the Transformer framework

The Boolean satisfiability (SAT) problem exhibits different structural features in various domains. Neural network models can be used as more generalized algorithms that can be learned to solve specific problems based on different domain data than traditional rule-based approaches. How to accurately...

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Vydané v:	PeerJ. Computer science Ročník 10; s. e2169
Hlavní autori:	Chang, Wenjing, Guo, Mengyu, Luo, Junwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States PeerJ. Ltd 08.08.2024 PeerJ Inc
Predmet:	Algorithms Algorithms and Analysis of Algorithms Artificial Intelligence Boolean satisfiability problem Computational linguistics Cross-attention Data Mining and Machine Learning Deep learning Electric transformers GRU Language processing Natural language interfaces Neural Networks Random 3-SAT Software Engineering Transformer Deep learning Transformer Cross-attention GRU Boolean satisfiability problem Random 3-SAT
ISSN:	2376-5992, 2376-5992
On-line prístup:	Získať plný text
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