A unifying framework for integrating domain knowledge into machine learning algorithms for multidisciplinary industrial applications

尽管监督学习方法在预测分析中表现优异，但迄今为止，它们只能从标记数据中学习。在这个过程中，人类的作用被限制在“仅仅是标签者”，专家的知识没有得到充分利用。这项研究设想了一个通用框架的发展，将专家的意见代表和整合到学习系统中，如果可能的话，将他们宝贵的知识转移到现实生活中的应用决策过程中。在接下来的五年里，我们计划开发一个新的统一框架，以有效的方式利用人类的建议来促进机器学习（ML）算法，我们打算为工业应用开发人类指导学习的新解决方案。拟议研究的长期目标包括深入调查和开发新颖，自动化，高效的实时ML算法，能够通过利用有监督和无监督方法的领域知识为工业问题提供解决方案。

Though supervised learning methods demonstrate superior performance in predictive analytics, to date, they learn only from labeled data. The role of human beings in the process has been restricted to “mere labeler,” and the knowledge of experts is not being fully utilized. This research envisages the development of a general framework to represent and integrate experts' opinions into learning systems, and, if possible, to transfer their invaluable knowledge for decision-making processes in real-life applications. Over the next five years, we plan to work on developing a novel unifying framework for utilizing human advice in an efficient manner to facilitate machine learning (ML) algorithms and we intend to develop novel solutions to human-guided learning for industrial applications. The long-term goals of the proposed research include deep investigation and development of novel, automated, efficient real-time ML algorithms capable of providing solutions to industrial problems by exploiting domain knowledge in both supervised and unsupervised methods. Innovation: Novel approaches for incorporating experts' knowledge into ML algorithms in noisy, structured domains will be developed to accelerate learning effective models in which, so far, humans have been merely used as labelers. We plan to develop a natural framework which will allow experts to encode their knowledge, both as general advice about the domain and as specific advice about particular examples. The proposed framework will offer generality by capturing different types of advice through preferential, cost function-based, qualitative constraints, as well as privileged information. Practical Applications: In practice, this research has the potential to significantly impact: (i) industrial applications in fields such as medical imaging and forensics, which can benefit from image segmentation and registration techniques and in which human advice can be exploited by using deep learning in an effective manner; (ii) the welding industry, by accelerating welding sequence optimization and reducing structural deformations; (iii) the manufacturing industry, by automating operations such as peg-hole insertion tasks; and (iv) the robotics industry, by providing solutions to robot inverse kinematics problems for higher degrees of freedom. Highly Qualified Personnel Training: The proposed research program intends to train students and produce data scientists with unique expertise in ML and artificial intelligence (AI), which will enable them to be world-class experts in the fields of ML, AI, and computer vision.