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.

Hysteretic q-learning :an algorithm for decentralized reinforcement learning in cooperative multi-agent teams

Abstract: Multi-agent systems (MAS) are a field of study of growing interest in a variety of domains such as robotics or distributed controls. The article focuses on decentralized reinforcement learning (RL) in cooperative MAS, where a team of independent learning robots (IL) try to coordinate their individual behavior to reach a coherent joint behavior. We assume that each robot has no information about its teammates' actions. To date, RL approaches for such ILs did not guarantee convergence to the optimal joint policy in scenarios where the coordination is difficult. We report an investigation of existing algorithms for the learning of coordination in cooperative MAS, and suggest a Q-learning extension for ILs, called hysteretic Q-learning. This algorithm does not require any additional communication between robots. Its advantages are showing off and compared to other methods on various applications: bi-matrix games, collaborative ball balancing task and pursuit domain.

Autonomous Agents and Multi-Agent Systems

Abstract: Autonomous Agents and Multi-Agent Systems www.springer.com/10458 Michael Luck and Carles Sierra, Editors-in-Chief Published by Springer. About this Issue Learning is a critical component for autonomous agents so that they can discover novel behaviors, adapt to a nonstationary world, and handle unanticipated events. As virtual and physical agents become more common, these learning agents need to handle interactions not only with an environment, but also with other agents. Multiagent learning thus becomes an indispensable component of building intelligent systems, even though much of the current learning research focuses on single-agent settings.

Multi-Agent Based Hierarchical Hybrid Control for Smart Microgrid

Abstract: This paper studies the smart control issue for an autonomous microgrid in order to maintain the secure voltages as well as maximize economic and environmental benefits. A control scheme called as multi-agent based hierarchical hybrid control is proposed versus the hierarchical control requirements and hybrid dynamic behaviors of the microgrid. The control scheme is composed of an upper level energy management agent, several middle level coordinated control agents and many lower level unit control agents. The goals of smart control are achieved by designed control strategies. The simulations are given to demonstrate the effectiveness of proposed smart control for an autonomous microgrid.

Fault-Tolerant Consensus of Multi-Agent System With Distributed Adaptive Protocol

Abstract: In this paper, fault-tolerant consensus in multi-agent system using distributed adaptive protocol is investigated. Firstly, distributed adaptive online updating strategies for some parameters are proposed based on local information of the network structure. Then, under the online updating parameters, a distributed adaptive protocol is developed to compensate the fault effects and the uncertainty effects in the leaderless multi-agent system. Based on the local state information of neighboring agents, a distributed updating protocol gain is developed which leads to a fully distributed continuous adaptive fault-tolerant consensus protocol design for the leaderless multi-agent system. Furthermore, a distributed fault-tolerant leader–follower consensus protocol for multi-agent system is constructed by the proposed adaptive method. Finally, a simulation example is given to illustrate the effectiveness of the theoretical analysis.

Dynamic coalition formation among rational agents

Abstract: Dynamic coalition formation (DCF) promises to be well suited for applications of ubiquitous and mobile computing. This article proposes a simulation-based DCF scheme designed to let rational agents form coalitions in dynamic environments.