Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.

Leaderless coordination via bidirectional and unidirectional time-dependent communication

Abstract: We study a simple but compelling model of n interacting agents with time-dependent, bidirectional and unidirectional communication. The model finds wide application in a variety of fields including swarming, synchronization and distributed decision making. In the model, each agent updates his current state based upon the current information received from other agents according to a simple weighted average rule. Necessary and/or sufficient conditions for the convergence of the individual agents' states to a common value are presented, extending recent results reported in the literature. Further, it is observed that more communication does not necessarily lead to better convergence and may eventually even lead to a loss of convergence, even for the simple models discussed in the present paper.

Consensus of heterogeneous first- and second-order multi-agent systems with directed communication topologies

Abstract: Summary In this paper, we consider the consensus problem for heterogeneous multi-agent systems composed of some first-order and some second-order dynamic agents in directed communication graphs. Consensus protocols are proposed for the second- and first-order dynamic agents, respectively. Under certain assumptions on the control parameters, for fixed communication topologies, necessary and sufficient conditions for consensus are given, and the consensus values of all agents are established. For switching topologies, sufficient conditions are given for all agents to reach consensus. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.

Multiagent commitment alignment

Abstract: Commitments provide a basis for understanding interactions in multiagent systems. Successful interoperation relies upon the interacting parties being aligned with respect to their commitments. However, alignment is nontrivial in a distributed system where agents communicate asynchronously and make different observations. We propose a formalization for commitments that ensures alignment despite asynchrony. This formalization consists of three elements: (1) a semantics of commitment operations; (2) messaging patterns that implement the commitment operations; and (3) weak constraints on agents' behaviors to ensure the propagation of vital information. We prove that our formalization ensures alignment. We illustrate the generality of our formalization with several real-life scenarios.

Spectrum management of cognitive radio using multi-agent reinforcement learning

Abstract: Wireless cognitive radio (CR) is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of these bands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power and spectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, and react to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach based spectrum management. Our approach uses value functions to evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, and compare the communication performance using different sets of learning parameters. We also apply Kanerva-based function approximation to improve our approach's ability to handle large cognitive radio networks and evaluate its effect on communication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network.