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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Bibliographic Details
Published in:	PeerJ. Computer science Vol. 10; p. e2169
Main Authors:	Chang, Wenjing, Guo, Mengyu, Luo, Junwei
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 08.08.2024 PeerJ Inc
Subjects:	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
Online Access:	Get full text
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