Towards Fully Integrated Deep Learning and Reinforcement Learning for General Spatial Domains.

迈向通用空间领域的完全集成深度学习和强化学习。

• 批准号: RGPIN-2018-04381
• 负责人: Crowley, Mark
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 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=712957
• 关键词: Towards; Fully; Integrated; Deep; Learning

Artificial intelligence (AI) and machine learning (ML) research develops techniques that enable automated prediction, data analysis and decision making using diverse data sources. The areas of application for AI/ML technologies are almost limitless and have the capacity to radically impact human society and almost every industry in the coming years. Before an AI/ML system can be used, it must be “trained” using relevant data from which it “learns” patterns etc. Two subfields of study that examine how to best train AI/ML systems are Deep Learning (DL) and Reinforcement Learning (RL). Thus far, DL and RL have been applied primarily to the analysis of photos, videos and video games but this omits patterns that occur in many important real world scenarios. For example, analyzing the spread of a forest fire involves tracking the temporary condition of burning across a landscape as it responds to vegetation, moisture, and altitude but irrevocably alters other vegetation, soil, and other local features. A Spatially Spreading Process (SSP) is said to exist in a domain when some local features change over time and space in this way based on proximity among objects but without resulting from the movement of an object or objects. It is a local change of some properties across a spatial landscape. Recently a convergence DL and RL research has begun to offer the potential for a quantum leap in the ability to use AI/ML for predicting/analyzing SSP data. However, a number of gaps remain which the proposed research program will investigate via three objectives: (1) develop a novel Deep RL framework that integrates actions directly into the basic calculations of deep neural networks for the purposes of prediction and decision making in 2D spatial environments, (2) investigate predictive classification for non-visual, spatial 3D environments using existing methods and a novel policy approach, and (3) formulate a more general theory of data augmentation for DL and RL algorithms that accounts for SSPs in both 2D and 3D environments. The results of this five year program will provide a base for the longer term goal of augmenting human decision making in a transparent and dependable way for problems with complex spatiotemporal dynamics and large-scale streaming data. The accuracy and efficiency of our methods will be validated on a range of domains with complex spatiotemporal structures including forest wildfire management, flood prediction and classification of Alzheimer's disease based on 3D diffusion MRI data. This program will contribute significantly to the growing footprint of forward-thinking AI/ML research coming out of universities in Ontario and Canada. The program will train ten graduate and five undergraduate students in rigorous research skills and the use of AI/ML software tools increasingly in demand within the information economy.

人工智能（AI）和机器学习（ML）研究开发了使用不同数据源进行自动预测、数据分析和决策的技术。AI/ML技术的应用领域几乎是无限的，并有能力在未来几年内从根本上影响人类社会和几乎所有行业。 在AI/ML系统可以使用之前，它必须使用相关数据进行“训练”，从中“学习”模式等。研究如何最好地训练AI/ML系统的两个子领域是深度学习（DL）和强化学习（RL）。到目前为止，DL和RL主要应用于照片，视频和视频游戏的分析，但这忽略了许多重要的真实的世界场景中出现的模式。例如，分析森林火灾的蔓延涉及跟踪整个景观的燃烧的临时条件，因为它响应于植被，湿度和海拔，但不可避免地改变了其他植被，土壤和其他当地特征。空间扩展过程（SSP）被认为存在于一个域中，当一些局部特征以这种方式基于对象之间的接近度而不是由一个或多个对象的移动引起的时间和空间变化时。它是一个空间景观中某些属性的局部变化。 最近，DL和RL的融合研究已经开始为使用AI/ML预测/分析SSP数据的能力提供巨大的飞跃。然而，仍然存在一些差距，拟议的研究计划将通过三个目标进行调查：（1）开发一种新的深度RL框架，将动作直接集成到深度神经网络的基本计算中，以实现在2D空间环境中进行预测和决策的目的，（2）使用现有方法和新的策略方法研究非视觉空间3D环境的预测分类，以及（3）制定用于DL和RL算法的更一般的数据增强理论，其考虑2D和3D环境中的SSP。 这项为期五年的计划的结果将为以透明和可靠的方式增强人类决策的长期目标提供基础，以解决复杂的时空动态和大规模流数据问题。我们的方法的准确性和效率将在一系列具有复杂时空结构的领域进行验证，包括森林野火管理，洪水预测和基于3D扩散MRI数据的阿尔茨海默病分类。 该计划将为安大略和加拿大大学的前瞻性AI/ML研究的不断增长做出重大贡献。该计划将培养10名研究生和5名本科生严格的研究技能和使用AI/ML软件工具在信息经济中的需求越来越大。