The proposed resilient consensus protocol integrates trust observations from physical communication channels and a confidence parameter to achieve consensus among legitimate agents despite the presence of malicious agents and uncertainty in trust information.
The core message of this article is to develop a novel privacy-preserving average consensus algorithm for unbalanced digraphs. The algorithm carefully embeds randomness in mixing weights and introduces an auxiliary parameter to mask the state-update rule in the initial iterations, while exploiting the intrinsic robustness of consensus dynamics to guarantee the exact average consensus.
Necessary and sufficient conditions for achieving scale-free state synchronization of homogeneous multi-agent systems via linear dynamic non-collaborative protocols, with and without utilizing local bounds on neighborhoods.
STEMFold is a spatiotemporal attention-based generative model that learns a stochastic manifold to predict the underlying unmeasured dynamics of a multi-agent system from observations of only a subset of visible agents.
This work introduces a hybrid approach that integrates Multi-Agent Reinforcement Learning with control-theoretic methods to ensure safe and efficient distributed strategies for dynamic network bridging tasks.
Agents need awareness of limitations and compromise for effective self-governance.
Sanctioning norm enforcement from an agent-centric perspective enhances norm compliance while preserving autonomy.
The author proposes a multi-agent system for collaborative task execution, highlighting the efficiency of centralized versus decentralized control approaches based on task dependencies. The study emphasizes the importance of optimal group size for enhancing system performance.