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RI:Small:Investigating techniques that couple Markov Logic and Deep Learning with applications to discovering strategies to improve STEM learning

RI：小：研究将马尔可夫逻辑和深度学习与应用相结合的技术，以发现改善 STEM 学习的策略

基本信息

批准号：
2008812
负责人：
Deepak Venugopal
金额：
$ 41.35万
依托单位：
University of Memphis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008812&HistoricalAwards=false
关键词：
RI Small Investigating techniques couple

项目摘要

The goal of this project is to develop novel techniques to integrate different but complementary approaches in artificial intelligence (AI). This research combines the strengths of Deep Neural Networks (DNNs) and Markov Logic Networks (MLNs) to address key shortcomings of those techniques when used by themselves. In particular, the proposed work will address the limitation of DNNs with respect to utilizing background knowledge in learning a model. The fact that DNNs typically do not utilize background knowledge explicitly often results in models that over-fit the training data and generalize poorly on new datasets. On the other hand, statistical relational models such as Markov Logic Networks (MLNs) encode complex background knowledge explicitly but lack inference and learning capabilities that are as scalable and accurate as DNN-based methods. The project will develop novel techniques in which MLNs provide the DNN with task-specific background knowledge which helps the DNN to learn more generalizable models. Further, this project will apply these novel techniques to significantly improve personalized learning in adaptive instructional systems (AISs) for STEM topics. The project will yield i) general-purpose open-source software for learning and inference that can be used by a broad range of application domains and ii) specific models for core tasks in AIS-based learning (e.g. inferring student problem-solving strategies) that can significantly improve the adaptive capabilities of AISs which results in better student engagement and learning. The project will impact a number of communities including machine learning, artificial intelligence, artificial intelligence in education, and educational data mining. The outcomes of our work will be widely disseminated through publications in top conferences and journals, presentations, a website, social media, and training materials for researchers and practitioners. Existing approaches that incorporate background knowledge into DNNs do so using a Bayesian framework where the types of priors are typically simple to ensure tractability of Bayesian inference. The main technical contribution of this project is to address this limitation by developing DNN models that incorporate rich relational knowledge specified in the form of an MLN. To do this, the project will i) develop new representations that encode symmetries (or exchangeability) in the MLN distribution as sub-symbolic embeddings, ii) develop efficient DNN-based learning algorithms for relational data by exploiting exchangeability of variables specified implicitly by the MLN and iii) develop interpretable generative models using Generative Adversarial Networks utilizing symmetries specified by the MLN to traverse across diverse modes in the distribution. The AIS tasks that will be developed as part of this project will use large-scale datasets and thus convincingly demonstrate the scalability of the proposed models in real-world problems. Further, the models developed for the AIS tasks will help us better understand student needs and learning processes which in turn can inform improvements of advanced educational technologies for STEM topics and help validate and refine human learning theories.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.

该项目的目标是开发新技术，以整合人工智能（AI）中不同但互补的方法。这项研究结合了深度神经网络（DNN）和马尔可夫逻辑网络（MLN）的优势，以解决这些技术在单独使用时的关键缺点。特别是，拟议的工作将解决DNN在利用背景知识学习模型方面的局限性。 DNN通常不显式地利用背景知识，这一事实通常会导致模型过度拟合训练数据，并且在新数据集上的泛化能力很差。另一方面，诸如马尔可夫逻辑网络（MLN）之类的统计关系模型显式地编码复杂的背景知识，但缺乏与基于DNN的方法一样可扩展和准确的推理和学习能力。该项目将开发新的技术，其中MLN为DNN提供特定于任务的背景知识，帮助DNN学习更通用的模型。此外，该项目将应用这些新技术来显着改善STEM主题自适应教学系统（AIS）中的个性化学习。该项目将产生i）用于学习和推理的通用开源软件，可用于广泛的应用领域，ii）基于AIS的学习中核心任务的特定模型（例如推断学生解决问题的策略），可以显着提高AIS的适应能力，从而提高学生的参与度和学习。该项目将影响许多社区，包括机器学习，人工智能，教育中的人工智能和教育数据挖掘。我们的工作成果将通过顶级会议和期刊上的出版物、演示文稿、网站、社交媒体以及研究人员和从业人员的培训材料广泛传播。将背景知识并入DNN的现有方法使用贝叶斯框架来这样做，其中先验的类型通常是简单的，以确保贝叶斯推理的易处理性。该项目的主要技术贡献是通过开发DNN模型来解决这一限制，该模型包含以MLN形式指定的丰富关系知识。为了做到这一点，该项目将i）开发新的编码对称的表示法（或交换）在MLN分布中作为子符号嵌入，ii）通过利用由MLN隐式指定的变量的交换来开发用于关系数据的高效的基于DNN的学习算法，以及iii）使用生成对抗网络开发可解释的生成模型，该生成对抗网络利用MLN指定的对称性来遍历不同的模式，分布。作为该项目的一部分，将开发的AIS任务将使用大规模数据集，从而令人信服地证明所提出的模型在现实问题中的可扩展性。此外，为AIS任务开发的模型将帮助我们更好地了解学生的需求和学习过程，从而为STEM主题的先进教育技术的改进提供信息，并帮助验证和完善人类学习理论。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。