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

将领域知识集成到多学科工业应用的机器学习算法中的统一框架

• 批准号: RGPIN-2020-05422
• 负责人: Saha, BaidyaNath
• 金额: $ 1.75万
• 依托单位: Concordia University of Edmonton
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717635
• 关键词: unifying; framework; integrating; domain; knowledge

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.

尽管有监督的学习方法在预测分析方面表现出了优越的性能，但到目前为止，它们只从标记数据中学习。人类在这一过程中的作用仅限于“贴标签者”，专家的知识没有得到充分利用。这项研究设想开发一个通用框架来表示专家的意见并将其整合到学习系统中，如果可能的话，将他们宝贵的知识转移到现实生活应用中的决策过程中。在接下来的五年里，我们计划致力于开发一个新的统一框架，以高效地利用人类的建议来促进机器学习(ML)算法，并打算为工业应用开发新的人类引导学习解决方案。该研究的长期目标包括深入研究和开发新的、自动的、高效的实时ML算法，该算法能够通过在监督和非监督方法中利用领域知识来提供工业问题的解决方案。 创新：将开发将专家知识整合到嘈杂、结构化领域的ML算法中的新方法，以加快学习有效模型的速度，在这些模型中，到目前为止，人类只是被用作标签者。我们计划开发一个自然的框架，允许专家将他们的知识编码为关于该领域的一般建议和关于特定例子的具体建议。拟议的框架将通过优先、基于成本函数、定性约束以及特权信息来获取不同类型的建议，从而提供一般性建议。 实际应用：在实践中，这项研究有可能产生重大影响： (1)医学成像和取证等领域的工业应用，可受益于图像分割和配准技术，并可通过有效地利用深度学习来利用人的意见； (2)焊接业，通过加速优化焊接顺序和减少结构变形； (3)制造业，通过自动操作，例如插入销孔任务；以及 (4)机器人工业，为更高自由度的机器人逆运动学问题提供解决方案。 高素质人才培养：拟议的研究计划旨在培养学生并培养出在ML和人工智能(AI)方面具有独特专业知识的数据科学家，这将使他们成为ML、AI和计算机视觉领域的世界级专家。