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: 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=716261
• 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 and Learning for Autonomous Operation. How can analytics, AI and ML be applied in management and control of distributed large-scale infrastructures to enable trustworthy self-optimizing, self-configuring, self-healing, self-protecting adaptive behavior? 3. Mechanism Design: Incentives and Pricing. How can high-level global objectives be translated into incentives and pricing signals that induce globally beneficial user resource consumption? Our methodology for CMSs integrates: 1. Algorithm design; 2. Implementation in software; 3. Proof-of-concept systems. Our research impacts will be: Secure, private, responsive and reliable delivery of applications in computing clouds, IoT and 5G networks Dramatic reduction in the number of vehicles on the road through multimode travel, autonomous vehicles and Mobility-on-Demand Mitigation and elimination of pollution in metropolises through renewable energy and vehicle electrification.

现代社会依赖于各种系统和基础设施提供的服务和应用程序：数据中心，跨越核心、边缘和传感器/物联网计算的云，互联网服务提供商网络，蜂窝网络，多式联运网络和电网。这些服务和应用基础设施的特征越来越多样化，其要求要求更复杂、精确和响应更快的控制。 例如，电网不再仅提供单向的能量输送，而是现在必须支持来自不同类型的可再生能源和传统能源的动态电力传输，以满足现有和新的电力需求，例如电动汽车。同样，在移动性方面，从私人汽车过渡到无缝的多模式旅行（火车，公共汽车，地铁，共享车辆和自行车，以及步行）。此外，移动性、能源、计算和网络基础设施必须考虑环境影响，如碳足迹和空气污染。 该研究计划的目标是促进对可信赖的自主智能控制和管理系统（CMS）的理解，创建，设计和操作，这些系统可以指导大型基础设施中的资源分配，以提供在质量，数量/体积和成本方面有所不同的服务和应用程序。CMS在需求服务和资源可用性之间进行调解。这些服务中有许多是使命关键型的，因此CMS在供需波动、故障和攻击的情况下是值得信赖和可靠的。基础设施的规模和复杂性要求CMS利用人工智能（AI）和机器学习（ML）来实现自主操作。我们应对四个挑战，以实现我们的CMS目标： 1.意图驱动的网络。如何将有关政策和意图的高层声明转化为高度可信的低层管理和控制行动？ 2.自主操作的分析和学习。如何将分析、人工智能和机器学习应用于分布式大规模基础设施的管理和控制，以实现值得信赖的自我优化、自我配置、自我修复、自我保护的自适应行为？ 3.机制设计：激励与定价。如何将高层次的全球目标转化为激励措施和定价信号，以诱导全球有益的用户资源消费？ 我们的CMS方法集成：1。算法设计; 2.软件实现; 3。概念验证系统。 我们的研究影响将是： 在计算云、物联网和5G网络中安全、私密、响应迅速和可靠地交付应用程序 通过多模式出行、自动驾驶汽车和按需移动，大幅减少道路上的车辆数量 通过可再生能源和车辆电气化减轻和消除大都市的污染。