Inspired by the empirical decision-making (EDM) phenomenon, wherein agents assimilate the opinion learned from social neighbors as their cognitive inertia and progressively rely on their own cognitive inertia sequence (CIS) for decision-making over time, we propose a novel CIS model paradigm and extend it based on the bounded confidence rule. In the extended CIS model, before agents obtain their acquired opinions, they will reconstruct the weight coefficients by evaluating the credibility of the opinions of social neighbors based on a comprehensive trust degree, composed of the opinion similarity and the centrality degree. Then, agents update their opinions through weighted aggregation of their CISs and acquired opinions. Finally, we apply the proposal to the Zachary?s karate club network, providing a comparison analysis between the extended CIS model and the HK model. Simulation results indicate that the number of opinion clusters increases as the trust threshold increases, and the extended CIS model has a shorter convergence time than the HK model, illustrating the effectiveness of the proposed model.

This paper introduces the liberating effect under group pressure into the Hegselmann-Krause (HK) model and proposes a novel expressed and private opinion dynamics model. Agents in the group hide their honest opinions because of the group pressure, and each agent has a private and expressed opinion. The liberating effect is divided into two stages. In the first stage, one agent in the group is the first to liberate when the number of times it feels pressure exceeds a specific limit and the cumulative pressure is the maximum. The liberating agent will express its opinion authentically, with private opinion consistent with the expressed opinion. In the second stage, the other agents in the group are influenced by the liberating neighbors and also liberate until the group evolution reaches a stable state. Through simulations, we study the effects of confidence level and pressure threshold on group opinion evolution. The experimental results show that both confidence level and pressure threshold are critical. All agents liberate when they are smaller than the critical value; when they are greater than the critical value, the liberating effect disappears. We also find that the liberating effect can accelerate the group opinion evolution.

In this study, a finite-time velocity-free rendezvous control method is considered for multiple autonomous underwater vehicle (AUV) systems with intermittent undirected communication. First, we develop a distributed finite-time observer for each AUV to estimate its own state information. Second, we design a rendezvous control algorithm that utilizes the estimated state information intermittently through a communication network in the absence of velocity measurement. A homogeneous method is used to prove that all AUVs in the group can achieve rendezvous in finite time for a network with intermittent communication, even without velocity measurements. The proposed method is shown to reduce the communication load of the system. More importantly, the control algorithm achieves the control goal of the system and is proven to be viable for many practical applications of multiple AUV systems from both economic and security perspectives. Finally, the effectiveness of the proposed control protocol is demonstrated via numerical simulations.

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

In this paper, a bipartite consensus problem is considered for a high-order multiagent system with unknown disturbances and cooperative-competitive interactions. Two control strategies are proposed to guarantee bipartite consensus for two cases with and without an exogenous system (called leader for simplicity), respectively. Linearly parameterized approaches are applied to describe the time-varying unknown disturbances. Distributed adaptive laws are then designed for the unknown parameters in the disturbances. Adaptive consensus controllers are designed in a fully distributed fashion, which does not rely on any global information. Simulation results are presented to demonstrate the formation of bipartite consensus under the proposed adaptive control strategies.

In this paper, from the perspective of opinion dynamics theory, we investigate the interaction mechanism of a group of autonomous agents in an e-commerce community (or social network), and the influence power of opinion leaders during the formation of group opinion. According to the opinion's update manner and influence, this paper divides social agents within a social network into two subgroups: opinion leaders and opinion followers. Then, we establish a new bounded confidence-based dynamic model for opinion leaders and followers to simulate the opinion evolution of the group of agents. Through numerical simulations, we further investigate the evolution mechanism of group opinion, and the relationship between the influence power of opinion leaders and three factors: the proportion of the opinion leader subgroups, the confidence levels of opinion followers, and the degrees of trust toward opinion leaders. The simulation results show that, in order to maximize the influence power in e-commerce, enhancing opinion leaders’ credibility is crucial.