arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.06922cs.AIcs.LGcs.LO
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Manthan:一种数据驱动的布尔函数综合方法

Manthan: A Data Driven Approach for Boolean Function Synthesis

Indian Institute of Technology Kanpur(印度理工学院坎普尔分校);National University of Singapore(新加坡国立大学)

Priyanka Golia, Subhajit Roy, Kuldeep S. Meel

摘要:

布尔函数综合是计算机科学中的一个基本问题,具有广泛的应用,并且在过去十年中引起了人们的极大兴趣,导致技术逐步改进。尽管激烈的算法开发,大量的问题仍然超出了最先进的技术的状态。受机器学习研究进展的启发,我们提出了Manthan,一种新的数据驱动的布尔函数综合方法。Manthan将功能合成视为一个分类问题,依赖于数据生成的约束采样的进步,以及自动推理的进步,以实现新的证明指导的改进和可证明的验证。在对609个基准测试进行广泛而严格的评估后,我们证明Manthan在当前技术水平上有了显著的改进,解决了356个基准测试,而280个是最先进的技术解决的;因此,我们证明了比现有技术水平增加了76个基准。此外,Manthan解决了60个目前最先进的技术都无法解决的基准。显著的性能改进,以及我们的详细分析,突出了未来在机器学习,约束采样和自动推理交叉领域的几个有趣的工作途径。

英文摘要:

Boolean functional synthesis is a fundamental problem in computer science with wide-ranging applications and has witnessed a surge of interest resulting in progressively improved techniques over the past decade. Despite intense algorithmic development, a large number of problems remain beyond the reach of the state of the art techniques. Motivated by the progress in machine learning, we propose Manthan, a novel data-driven approach to Boolean functional synthesis. Manthan views functional synthesis as a classification problem, relying on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement and provable verification. On an extensive and rigorous evaluation over 609 benchmarks, we demonstrate that Manthan significantly improves upon the current state of the art, solving 356 benchmarks in comparison to 280, which is the most solved by a state of the art technique; thereby, we demonstrate an increase of 76 benchmarks over the current state of the art. Furthermore, Manthan solves 60 benchmarks that none of the current state of the art techniques could solve. The significant performance improvements, along with our detailed analysis, highlights several interesting avenues of future work at the intersection of machine learning, constrained sampling, and automated reasoning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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