FMitF: Collaborative Research: Synergies between Program Synthesis and Neural Learning of Graph Structures

在种类繁多且不断增长的应用中，一个具有挑战性的问题涉及自动生成满足所需功能要求的计算机程序。最近出现的两种有希望的和互补的方法来解决这个问题是程序综合和神经学习。该项目旨在将这两种方法协同结合起来，以提高程序员的生产率和软件质量。该项目还旨在培养形式方法和机器学习相结合的研究生，通过实习吸引本科生参与研究，并以公开可用的课程材料和开源软件制品的形式传播结果。程序合成确保生成的程序在逻辑规范方面是正确的。此外，通过改变规格，用户可以很容易地将合成器从不想要的节目引导到想要的节目。另一方面，神经学习可以处理不可能通过逻辑规范提供的用户需求--这一事实从神经网络在自然语言处理、计算机视觉和机器人等领域的成功中可见一斑。此外，神经网络的伸缩性非常好，因为它们能够学习在不同程序中重复的潜在模式。该项目建立在程序合成最新进展的基础上，通过开发新的基于学习的机制，实现灵活的规范、更丰富的验证器和可伸缩的解算器。在机器学习领域，它使深度神经网络能够提供对丰富结构化数据进行推理时通常需要的正确性保证。通过这样做，它开发了新的架构和方法，用于表征学习、强化学习和有限数据的学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A challenging problem in a diverse and growing body of applications concerns automatically generating computer programs that satisfy desired functional requirements. Two promising and complementary approaches that have recently emerged to address this problem are program synthesis and neural learning. This project aims to synergistically combine the two approaches to improve the productivity of programmers and the quality of software. The project also aims to train graduate students at the intersection of formal methods and machine learning, engage undergraduate students in research through internships, and disseminate results in the form of publicly available course materials and open-source software artifacts.Program synthesis ensures that the generated program is correct with respect to a logical specification. Moreover, users can easily guide the synthesizer away from an undesired program and towards a desired one, by changing the specification. On the other hand, neural learning can handle user requirements that are impossible to provide via a logical specification -- a fact highlighted by the success of neural networks in domains such as natural language processing, computer vision, and robotics. Moreover, neural networks scale extremely well, by virtue of their ability to learn latent patterns that repeat across different programs. This project builds upon recent progress in program synthesis by developing novel learning-based mechanisms that enable flexible specifications, richer verifiers, and scalable solvers. In the realm of machine learning, it enables deep neural networks to provide correctness guarantees that are typically required when reasoning about rich structured data. In doing so, it develops novel architectures and methodologies for representation learning, reinforcement learning, and learning with limited data.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.