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导致了金融、营销、零售和医疗保健等领域的成功应用。 然而，许多应用领域仍然无法达到这些方法，当孤立地应用。 例如，在医疗机器人领域，开发能够识别、引导、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，该系统允许在存在不可靠带宽通信的情况下远程执行挽救生命的手术。对于这样的系统，已经开发了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将允许生成可行的手术计划;但是目前，在复杂环境下没有机制来表示和评估这种知识。为了利用现实生活中的应用程序所需的功能，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。先前的研究表明，约束推理和机器学习的集成是必不可少的安全和高效的技术在这一领域的发展。该项目旨在推动机器学习和约束推理技术的发展，并将促进机器学习、约束学习和机器人学领域的形式研究和应用研究的交叉。该项目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代表物理和操作要求的约束推理模块来增强机器学习算法。具体来说，这项研究提出将决策图（一种流行的约束推理工具）作为深度神经网络中的完全可微层嵌入。 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比传统机器学习方法拥有更小的建模空间，使机器学习算法能够更快地学习并更好地推广。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评估被认为值得支持影响审查标准。

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.