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.

该项目的目的是开发新技术来整合人工智能（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，以解决自己使用时这些技术的关键缺点。特别是，拟议的工作将解决DNN在学习模型中利用背景知识方面的局限性。 DNN通常不明确利用背景知识的事实通常会导致模型过度拟合培训数据并在新数据集上概括不良。另一方面，统计关系模型（例如马尔可夫逻辑网络（MLN））明确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论和学习能力。该项目将开发新型技术，其中MLN为DNN提供特定于任务的背景知识，这些知识可帮助DNN学习更多可概括的模型。此外，该项目将应用这些新技术来显着改善STEM主题的自适应教学系统（AISS）的个性化学习。该项目将产生i）通用开源软件用于学习和推理，可以由广泛的应用领域和ii）基于AIS的学习中的核心任务（例如，推断学生问题解决问题的策略）的特定模型可以显着提高AISS的自适应能力，从而在更好的学生互动和学习中导致了更好的学生的互动和学习。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。我们的工作结果将通过顶级会议，期刊，演示文稿，网站，社交媒体和研究人员和从业者培训材料的出版物广泛传播。将背景知识纳入DNN的现有方法是使用贝叶斯框架进行的，即先验的类型通常简单以确保贝叶斯推论的障碍。该项目的主要技术贡献是通过开发包含以MLN形式指定的丰富关系知识的DNN模型来解决这一限制。为此，项目将i）开发新的表示，将MLN分布中的对称（或交换性）作为亚答一下嵌入，ii）ii）ii）ii）开发有效的基于DNN的学习算法，通过使用MLN和III使用Sysemiate Introvied Introvied Interifatied Unterifatied Unterifatied Unterifatied Untifielatied Aversar和III）来开发关系数据，以利用交换性，并开发有效的关系数据，并开发出有效的关系。 MLN跨越分布的不同模式。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，因此令人信服地证明了在现实世界中提出的模型的可扩展性。此外，为AIS任务开发的模型将有助于我们更好地了解学生的需求和学习过程，从而可以为STEM主题的先进教育技术提供改进，并有助于验证和完善人类学习理论。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。