FMitF: Collaborative Research: Track I: Embedding Constraint Reasoning in Machine Learning for Better Prediction and Decision-making

大规模数据驱动的机器学习和优化方法的出现已经在金融、营销、零售和医疗保健等不同领域取得了成功的应用。然而，当单独应用这些方法时，许多应用程序域仍然是这些方法无法触及的。例如，在医疗机器人领域，开发能够识别、指导、支持或纠正外科手术的系统至关重要。这对于下一代创伤护理系统尤其重要，因为它允许在不可靠的带宽通信存在的情况下远程进行挽救生命的手术。对于这样的系统，已经开发出可以识别某些模式的机器学习模型，但它们无法在复杂的物理或操作约束下执行。另一方面，使用基于约束的优化方法可以生成可行的手术计划；但目前还没有一种机制能够在复杂的环境下表达和评价这些知识。为了利用实际应用所需的功能，该项目开发了一种集成方法，将约束推理嵌入机器学习（ECOR-ML）。研究人员打算证明ECOR-ML在医疗机器人领域的有效性。已有的研究表明，约束推理和机器学习的结合对于该领域安全高效的技术发展至关重要。该项目旨在推进机器学习和约束推理技术，并将促进机器学习、约束学习和机器人技术领域的正式研究和应用研究的相互促进。本项目中的方法为结构化领域的机器学习提供了一种可扩展的方法。其核心思想是用一个表示物理和操作需求的约束推理模块来增强机器学习算法。具体而言，本研究提出将决策图（一种流行的约束推理工具）作为深度神经网络的完全可微层嵌入。通过强制约束，生成模型的输出现在可以提供安全性、正确性和/或公平性的保证。此外，ECOR-ML具有比传统机器学习方法更小的建模空间，允许机器学习算法更快地学习和更好地泛化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The emergence of large-scale data-driven machine learning and optimization methods has led to successful applications in areas as diverse as finance, marketing, retail, and health care. Yet, many application domains remain out of reach for these methods, when applied in isolation. In the area of medical robotics, for example, it is crucial to develop systems that can recognize, guide, support, or correct surgical procedures. This is particularly important for next-generation trauma care systems that allow life-saving surgery to be performed remotely in the presence of unreliable bandwidth communications. For such systems, machine learning models have been developed that can recognize certain patterns, but they are unable to perform under complex physical or operational constraints. Using constraint-based optimization methods, on the other hand, would allow the generation of feasible surgical plans; but currently, there is no mechanism to represent and evaluate such knowledge under complex environments. To leverage the required capabilities for real-life applications, this project develops an integrated method that Embeds Constraint Reasoning in Machine Learning (ECOR-ML). The researchers intend to demonstrate the effectiveness of ECOR-ML in the context of medical robotics. Prior research indicates that the integration of constraint reasoning and machine learning is essential for the development of safe and efficient technologies in this domain. The project aims to advance both machine learning and constraint reasoning technology, and will promote the cross-fertilization of formal and applied research in the areas of machine learning, constraint learning, and robotics.The approach in this project provides a scalable method for machine learning over structured domains. The core idea is to augment machine learning algorithms with a constraint reasoning module that represents physical and operational requirements. Specifically, this research proposes to embed decision diagrams, a popular constraint reasoning tool, as a fully-differentiable layer in deep neural networks. By enforcing the constraints, the output of generative models can now provide assurances of safety, correctness, and/or fairness. Moreover, ECOR-ML possesses a smaller modeling space than traditional machine learning approaches, allowing machine learning algorithms to learn faster and generalize better.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.