Impulsive Cluster Anticonsensus of Discrete Multiagent Linear Dynamic Systems

Cluster anticonsensus is another important type consensus of multiagent systems. In this paper, we investigate the problem of impulsive cluster anticonsensus of discrete multiagent linear dynamic systems. Firstly, an impulsive protocol is designed to achieve the cluster anticonsensus. Then sufficient conditions are given to guarantee the cluster anticonsensus of the discrete multiagent linear dynamic system based on the Q-theory. Numerical simulation shows the effectiveness of our theoretical results

Distributed Impulsive Consensus of the Multiagent System without Velocity Measurement

This paper deals with the distributed consensus of the multiagent system. In particular, we consider the case where the velocity (second state) is unmeasurable and the communication among agents occurs at sampling instants. Based on the impulsive control theory, we propose an impulsive consensus algorithm that extends some of our previous work to account for the lack of velocity measurement. By using the stability theory of the impulsive system, some necessary and sufficient conditions are obtained to ensure the consensus of the controlled multiagent system. It is shown that the control gains, the sampled period and the eigenvalues of Laplacian matrix of communication graph play key roles in achieving consensus. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed algorithm

Intent Recognition in Multi-Agent Systems: Collective Box Pushing and Cow Herding

In a multi-agent system, an idle agent may be available to assist other agents in the system. An agent architecture called intent recognition is proposed to accomplish this with minimal communication. In order to assist other agents in the system, an agent performing recognition observes the tasks other agents are performing. Unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. This study focuses on the key research questions of: (1) What are intent recognition systems? (2) How can these be used in order to have agents autonomously assist each other effectively and efficiently? A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using two experimental series in the domains of Box Pushing, where agents attempt to push boxes to specified locations; and Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In both sets of experimental series, intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform, which was seen in both experimental series. Intent recognition agents were also able to outperform plan recognition agents by sometimes reducing task completion time in the Box Pushing domain and consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains