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

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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.