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

该项目将为存在对手的多智能体系统的分布式协调和优化开发以信任为中心的弹性的理论基础。这种弹性是通过代理通过本地通信学习邻居的可信度来实现的，这使得它们能够减轻对抗行为的有害影响。特别是，代理可以识别和隔离对手，因此，代理能够维持所需的系统性能。这种有弹性的自主多智能体系统可能在未来部署自动车队、自动交付系统（如机器人和无人机）以及我们家中的物理和连接设备中发挥重要作用。该方法旨在为有效利用网络中的随机“侧信息”建立理论基础和分析框架，从而为多智能体优化问题提供可证明的更强的弹性保证。恶意行为通过概率链接损坏模型来处理，该模型将攻击与其对系统的影响区分开来。这种分离是至关重要的，因为它支持使用统计推断技术开发信任模型。该模型适用于研究损坏数据对多智能体协调和优化任务弹性的影响。这项工作的重点是推导弹性分布式优化算法和弹性共识协议，它们可以容忍超过一半的网络连接是恶意的；一个经典的要求，这个项目旨在放松。该项目的具体目标是开发用于分布式检测攻击、减轻攻击以及在存在对手的情况下表征可实现的性能保证的方法。贡献是一个统一的理论，用于理解如何使用代理间通信来检测和隔离恶意代理，同时可证明地量化它们对系统性能的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.