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

神经符号编程是一种新兴范例，旨在通过将深度学习与经典逻辑推理相结合来解决深度学习的基本挑战。该项目旨在通过开发方法、算法、软件实现、基准测试和案例研究来实现神经符号编程的前景。为了执行拟议的研究，该团队汇集了具有机器学习、逻辑推理、形式化方法和编程系统专业知识的研究人员，并将与放射学研究人员合作开发医疗保健的实际应用。该项目的主要成果将是神经符号编程的开源框架，这将引起机器学习和逻辑推理研究人员的技术兴趣。从更广泛的意义上讲，该项目有助于实现可信人工智能 (AI) 的愿景，并使其能够部署在医疗保健等关键应用中。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叉点建立一个研究人员社区，促进研究成果与教育的紧密结合，并促进多样性。 该项目以名为 Scallop 的神经符号编程框架为中心，它将神经架构与基于声明性规则的逻辑编程语言集成在一起。这种设计可以方便地指定具有挑战性的任务，例如通过将所需计算适当分解为神经和符号组件来进行视觉问答。为了解决为此类神经符号程序开发基于端到端梯度下降的学习算法的核心挑战，该研究按照三个基本主题进行组织：1）符号推理结构，允许指定丰富的领域知识，同时实现高效的推理和学习； 2）基于溯因推理和数据来源的思想的神经符号程序的可扩展学习； 3）神经符号程序语义鲁棒性的理论和技术。 补充研究任务包括开发 Scallop 编译器和工具链、从广泛的计算任务中收集神经符号学习基准、实证评估，以及探索通过将基于神经网络的乳房 X 光图像处理与基于规则的专家知识相结合来改进乳腺癌风险评估的应用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.