SHF: Small: Boosting Reasoning in Boolean Networks with Attributed Graph Learning

SHF：小：通过属性图学习增强布尔网络的推理

• 批准号: 2008144
• 负责人: Cunxi Yu
• 金额: $ 38.17万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008144&HistoricalAwards=false
• 关键词: SHF; Small; Boosting; Reasoning; Boolean; SHF; Small; Boosting; Reasoning; Boolean

Boolean networks, mostly represented as graphs, have emerged as an effective logical representation to model not only the computational processes but also several phenomena from science and engineering, such as genetic analysis, electronic design automation, formal verification, etc. However, Boolean networks used in modern science and engineering applications can be extremely large with complex structures, which makes them less practical for real-world applications. For example, Boolean networks for optimizing logic circuits can have billions of vertices and cannot be effectively handled using traditional algorithms. Recent years have seen a widespread application of machine-learning (ML) techniques to various problems over graphs, namely graph learning, which has been successfully applied to accelerate applications by exploiting graph features found in social-network prediction and drug analysis. This project aims to develop a systematic framework that leverages graph learning to reason about Boolean networks, including dataset design, learning-algorithm development, training models, system integration, and evaluation over various application domains. The framework will be implemented in an extensible platform that can be used for a variety of applications in science and engineering. This project will create unique education and outreach opportunities for both academic and industrial participants, which involve mentoring of graduate and undergraduate students, innovation in teaching with investigator’s new courses in electronic design and deep learning, and attracting and preparing high-quality researchers with diverse backgrounds.The team of researchers will develop a set of novel algorithms in graph fusion, graph coarsening and refinement, and graph neural networks, to achieve high-quality and scalable embeddings for reasoning about functional, high-level abstractions of billion-node Boolean networks. The methods in this project will sit between the classical symbolic techniques in formal methods and ML in order to benefit both research communities in many domains, such as verification and synthesis, bioinformatics, artificial intelligence, and security. Specifically, the investigator plans to leverage and advance ML in symbolic-reasoning tasks, such that it can perform truly scalable Boolean reasoning analogously to traditional symbolic-reasoning approaches. The developments of this project will focus on novel algorithms in graph fusion and neural network architectures, domain-specific compression algorithms, end-to-end system integration, and large-scale system-level parallelism. In addition, the framework will be evaluated in algorithmic design-space exploration, targeting Boolean satisfiability solving and Boolean optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

布尔网络（主要是图形）已成为一种有效的逻辑表示形式，它不仅模拟了计算过程，而且还建模了科学和工程等的几种现象，例如遗传分析，电子设计自动化，正式验证等。但是，在现代科学和工程应用中使用的布尔网络使用的布尔网络可以使其更大的复杂结构具有更大的实用，从而使其更加实用，这使得真正的实用是真正的实用。例如，用于优化逻辑电路的布尔网络可以具有数十亿个顶点，并且无法使用传统算法有效地处理。近年来，机器学习（ML）技术在图形上的各种问题（即图形学习）中广泛应用，通过利用在社交网络预测和药物分析中发现的图形特征，已成功地应用了图形学习。该项目旨在开发一个系统的框架，该框架利用图形学习来推理有关布尔网络的推理，包括数据集设计，学习 - 算法开发，培训模型，系统集成以及对各种应用程序域的评估。该框架将在可扩展的平台中实施，该平台可用于科学和工程领域的各种应用。该项目将为学术和工业参与者创造独特的教育和外展机会，涉及对研究生和本科生的心理化，与研究者在电子设计和深度学习方面的新课程进行教学的创新，并吸引和准备高质量的研究人员具有潜水员的背景。用于推理数十亿节点布尔网络的功能高级抽象的嵌入。该项目中的方法将位于正式方法中的经典符号技术和ML之间，以使许多领域的研究社区受益，例如验证和综合，生物信息学，人工智能和安全性。具体而言，研究人员计划利用和提高ML符号策划任务，以便可以类似地执行与传统的象征性方法相似的可扩展布尔推理。该项目的发展将集中在图融合和神经网络体系结构，特定领域的压缩算法，端到端系统集成以及大型系统级并行性中的新算法。此外，该框架将在算法设计空间探索中进行评估，针对布尔满意度解决方案和布尔优化。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。