Trustworthy Autonomous Intelligent Systems

值得信赖的自主智能系统

• 批准号: RGPIN-2019-06866
• 负责人: LeonGarcia, Alberto
• 金额: $ 4.66万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739153
• 关键词: Trustworthy; Autonomous; Intelligent; Systems

Modern society relies on services and applications that are delivered by a variety of systems and infrastructures: data centers, clouds spanning core, edge, and sensor/IoT computing, Internet service provider networks, cellular networks, multimodal transportation networks, and power grids. These services and applications infrastructures are increasingly more diverse in their characteristics and their requirements call for more sophisticated, precise and responsive control. For example, power grids no longer provide only unidirectional delivery of energy, but must now support dynamic power transfer from different types of renewable and conventional energy sources to meet existing and new power demands, such as electric vehicles. As well, in mobility there is a transition from private cars to the seamless multimode travel (trains, buses, subways, shared vehicles and bicycles, and walking). Furthermore, mobility, energy, computing and networking infrastructures must consider environmental impacts such as carbon footprint and air pollution. The goal of this research program is to advance the understanding, creation, design, and operation of trustworthy autonomous intelligent control and management systems (CMSs) that direct the allocation of resources in large-scale infrastructures to enable the delivery of services and applications that vary in terms of quality, quantity/volume, and cost. The CMSs mediate between the demand services and availability of resources. Many of these services are mission critical, and so the CMS be trustworthy and reliable, in the presence of fluctuations in supply and demand, and of failures and attacks. The scale and complexity of infrastructures require that the CMS leverage artificial intelligence (AI) and machine learning (ML) to enable autonomous operation. We address four challenges to achieve our CMS goal:  1.Intent-Driven Networking. How can high-level statements regarding policies and intent be translated into high-fidelity low-level management and control actions?  2.Analytics

各种系统和基础设施的救济和应用程序：跨越核心的云，传感器/IoT计算，互联网服务提供商网络，蜂窝网络，多模式运输网络和电网例如，应用程序是增加的特征及其特征，例如，来自不同类型的可再生能源和常规能源的福希斯特人D。公共汽车，地铁，共享的车辆和步行）。将大规模基础架构分配给服务和术语方面有所不同的服务任务至关重要，因此基础架构的信任和可靠的规模和复杂性要求CMS利用人工智能（AI）和机器学习（ML）来实现自动操作。 g政策和意图被转化为高保真和控制动作？