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

该项目将开发以信任为中心的弹性的理论基础，用于在存在对手的情况下多代理系统的分布式协调和优化。弹性是通过智能体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不利影响。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主车队、自动化交付系统（如机器人和无人机），以及我们家中的物理和连接设备。该方法是建立有效利用网络中发现的随机“边信息”的理论基础和分析框架，为了达到多代理优化问题的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解决，该模型在攻击及其对系统的影响之间提供了重要的分离。这种分离是至关重要的，因为它可以使用统计推断技术开发信任模型。由此产生的模型是适合于研究损坏的数据对多智能体协调和优化任务的弹性的影响。这项工作的重点是导出弹性分布式优化算法和弹性共识协议，可以容忍超过一半的网络连接是恶意的，这是一个经典的要求，本项目旨在放松。该项目的具体目标是开发分布式检测攻击，攻击缓解和可实现的性能保证在对手的存在的表征的方法。这一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，同时可证明量化其对系统性能的影响。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.