SHF: Medium: Scallop: A Neurosymbolic Programming Framework for Combining Logic with Deep Learning

SHF：Medium：Scallop：一种将逻辑与深度学习相结合的神经符号编程框架

• 批准号: 2313010
• 负责人: Mayur Naik
• 金额: $ 120万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313010&HistoricalAwards=false
• 关键词: SHF; Medium; Scallop; Neurosymbolic; Programming

Neurosymbolic programming is an emerging paradigm that aims to address fundamental challenges for deep learning by combining it with classical logical reasoning. This project aims to realize the promise of neurosymbolic programming by developing a methodology, algorithms, software implementation, benchmarks, and case studies. To execute the proposed research, the team brings together researchers with expertise spanning machine learning, logical reasoning, formal methods, and programming systems, and will collaborate with a radiology researcher on a real-world application in healthcare. The primary outcome of the project will be an open-source framework for neurosymbolic programming that will be of technical interest to researchers in both machine learning and logical reasoning. In a broader sense, the project contributes to the vision of Trustworthy Artificial Intelligence (AI), and enables its deployment in critical applications such as healthcare. Through education and knowledge transfer activities, the project will build a community of researchers at the intersection of machine learning and logic, to facilitate tight integration of research results with education, and to promote diversity. The project is centered around a neurosymbolic programming framework, called Scallop, that integrates neural architectures with a declarative rule-based logic programming language. Such a design allows convenient specification of challenging tasks such as visual question answering by a suitable decomposition of the desired computation into neural and symbolic components. To address the core challenge of developing end-to-end gradient-descent-based learning algorithms for such neurosymbolic programs, the research is organized along three foundational themes: 1) symbolic reasoning constructs that allow specifying rich domain knowledge yet enable efficient inference and learning; 2) scalable learning for neurosymbolic programs based on ideas rooted in abductive inference and data provenance; and 3) theory and techniques for semantic robustness of neurosymbolic programs. Complementary research tasks include development of the Scallop compiler and toolchain, collection of benchmarks for neurosymbolic learning from a wide range of computational tasks, empirical evaluation, and exploring an application to improve breast cancer risk assessment by integrating neural-network-based image processing of mammograms with rule-based expert knowledge.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.

Neurosambolic编程是一个新兴的范式，旨在通过将其与经典的逻辑推理相结合来应对深度学习的基本挑战。该项目旨在通过开发一种方法，算法，软件实施，基准和案例研究来实现神经成像编程的承诺。为了执行拟议的研究，团队将研究人员汇集了跨越机器学习，逻辑推理，正式方法和编程系统的专业知识，并将与放射学研究人员合作就医疗保健中的现实应用程序进行合作。该项目的主要结果将是用于神经束编程的开源框架，在机器学习和逻辑推理方面，研究人员都将引起技术兴趣。从广义上讲，该项目有助于值得信赖的人工智能（AI）的愿景，并使其在医疗保健等关键应用程序中的部署。通过教育和知识转移活动，该项目将在机器学习和逻辑的交汇处建立一个研究人员社区，以促进研究结果与教育的紧密整合并促进多样性。 该项目围绕着一个名为Scallop的神经肯定编程框架，该框架将神经体系结构与基于声明性的规则逻辑编程语言集成在一起。这样的设计可以方便地规范具有挑战性的任务，例如通过将所需计算的适当分解为神经和符号组件的可视化问题回答。为了解决为此类神经肯定计划开发基于端到端梯度逐渐梯度的学习算法的核心挑战，该研究是按照三个基础主题进行组织的：1）符号推理构建体，允许指定丰富的领域知识但可以启用有效的推论和学习； 2）基于扎根于绑架推理和数据出处的思想的神经符号计划的可扩展学习； 3）神经符号程序语义鲁棒性的理论和技术。 互补的研究任务包括开发扇贝编译器和工具链，从广泛的计算任务中收集用于神经成像的基准学习，用于从各种计算任务中学习，经验评估，经验评估，以及探索应用于改善乳腺癌风险评估的应用，通过将基于基于乳的乳房的图像处理与基于规则的知识相结合，通过基于规则的知识来反映nsf的基础知识。优点和更广泛的影响审查标准。