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预测布尔公式的可满足性,通过在变压器框架中整合封闭的反复单位 (GRU)

Wenjing Chang¹, Mengyu Guo¹, Junwei Luo¹

¹School of Software, Henan Polytechnic University, Jiaozuo, Henan, China.

PeerJ. Computer science

|August 15, 2024

概括

TG-SAT是一个新的框架,结合了变压器和门式循环神经网络 (GRU),准确地预测了布尔满意度 (SAT) 问题解决方案. 这种方法通过在监督较弱的环境中学习结构特征来提高SAT解析器的准确性.

科学领域:

人工智能的人工智能
计算机科学计算机科学
计算复杂性计算复杂性

背景情况:

布尔满足性 (SAT) 问题具有各种结构特征,对于解决这些问题至关重要.
神经网络为SAT问题提供了通用的学习能力,超越了传统的基于规则的方法.
准确识别SAT问题结构是有效神经网络解决者的关键.

研究的目的:

提出TG-SAT,这是一个端到端的框架,用于预测SAT问题的满足性.
为了使神经网络能够在弱监督的环境中学习SAT问题的结构特征.
为了提高SAT解决器的准确性和性能.

主要方法:

TG-SAT采用了集成与门式循环神经网络 (GRU) 的变压器架构.
SAT问题以非定向图形的形式编码,以捕获结构信息.
GRU更新了变压器中的节点嵌入,使用字面和句子之间的交叉注意力得分来进行加权节点表示.
该模型被训练为分类器,以预测可满足性.

主要成果:

在随机的3SAT问题上,TG-SAT比NeuroSAT提高了2%-5%的准确性.
该模型在SR(N) 基准上表现优于现有方法.
TG-SAT实现了更高的预测准确度,特别是在更复杂的SAT问题上.

结论:

TG-SAT有效地学习SAT问题的结构特征,以改善可满足性预测.
拟议的框架为基于学习的SAT解决方案提供了重大进展.
TG-SAT显示出解决复杂计算问题的巨大潜力.

关键词:

布尔式满足问题布尔式满足问题交叉注意力交叉注意力深度学习是一种深度学习.在这里,GRU GRU GRU 随机的 3-SAT 时间变压器变压器变压器

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