Semi-Autonomous Networks: Network Resilience and Adaptive Trees

This paper examines the dynamics of a networked multi-agent system operating with a consensus-type coordination algorithm that can be influenced by external agents. We refer to this class of networks as semi-autonomous. Within such a class, we consider a network's resilience to the influence of external agents delivering a test signal, namely a Gaussian noise with a given mean. Specifically, we examine the resultant mean and variance of the states of the agents in the network, via metrics dubbed as mean and variance resilience, as well as relate these quantities to circuit-theoretic notions of the network. These metrics are then used to propose adaptive protocols for tree graphs to increase or decrease the mean and variance resilience. Finally, a hybrid protocol is proposed which is shown to have a guaranteed performance using game-theoretic techniques. All protocols involve decentralized edge swaps that can be performed in parallel, asynchronously, and require only local agent information of the graph structure.