Collaborative Research: SaTC: CORE: Medium: Foundations of Trust-Centered Multi-Agent Distributed Coordination

协作研究：SaTC：核心：媒介：以信任为中心的多智能体分布式协调的基础

基本信息

批准号：
2147641
负责人：
Angelia Nedich
金额：
$ 50.48万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147641&HistoricalAwards=false
关键词：
Collaborative Research SaTC CORE Medium

项目摘要

This project will develop the theoretical foundations of trust-centered resilience for distributed coordination and optimization of multi-agent systems in the presence of adversaries. The resilience is to be achieved by agents’ learning trustworthiness of their neighbors through local communications, which allows them to mitigate the detrimental impact of adversarial actions. In particular, the agents can identify and isolate the adversaries and, thus, the agents are able to sustain the desired system performance. Such resilient autonomous multi-agent systems are likely to play an important role in the future deployment of autonomous vehicle fleets, automated delivery systems (such as robots and drones), as well as physical and connected devices in our homes.The approach is to establish the theoretical foundations and analytical framework for efficient exploitation of stochastic "side information" found in the network, in order to arrive at provably stronger guarantees of resilience for multi-agent optimization problems. Malicious actions are addressed through probabilistic link-corruption models, which provides an important separation between the attack and its impact on the system. This separation is critical as it enables the development of trust models using statistical inference techniques. The resulting model is suitable for studying the impact of corrupted data on the resilience of multi-agent coordination and optimization tasks. The focus in this work is on deriving resilient distributed optimization algorithms and resilient consensus protocols that can tolerate more than half of the network connectivity being malicious; a classical requirement that this project aims to relax. Specific objectives of the project are to develop methods for distributed detection of an attack, attack mitigation, and characterization of attainable performance guarantees in the presence of adversaries. The contribution is a unified theory for understanding how inter-agent communications can be used to detect and isolate malicious agents, while provably quantifying their impact on system performance.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.

该项目将在存在对手的情况下为以信任为中心的弹性来开发以信任为中心的弹性和对多代理系统的优化的理论基础。代理商通过当地通信对邻居的学习信任度来实现韧性，这使他们能够减轻对抗性行动的有害影响。特别是，代理商可以识别和隔离对手，因此，代理商能够维持所需的系统性能。这种弹性的自主多代理系统在未来的自动驾驶羊毛，自动递送系统（例如机器人和无人机）以及家中的物理和连接设备的未来部署中可能发挥重要作用优化问题。恶意行动是通过概率的链接腐败模型来解决的，该模型在攻击及其对系统的影响之间提供了重要的分离。这种分离至关重要，因为它可以使用统计推理技术来开发信任模型。最终的模型适合研究损坏数据对多机构协调和优化任务的弹性的影响。这项工作的重点是得出弹性分布式优化算法和弹性共识协议，这些协议可以忍受一半以上的网络连接性是恶意的；该项目旨在放松的经典要求。该项目的具体目标是开发用于分布攻击，缓解攻击和表征在对手存在下可达到的绩效保证的方法。该贡献是一种统一的理论，用于了解如何使用跨代理通信来检测和隔离恶意药物，同时可能量化其对系统性能的影响。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。