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.

Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings

Abstract: Smart and energy-efficient buildings have recently become a trend for future building industry. The major challenge in the control system design for such a building is to minimize the power consumption without compromising the customers comfort. For this purpose, a hierarchical multiagent control system with an intelligent optimizer is proposed in this study. Four types of agents, which are switch agent, central coordinator-agent, local controller-agent, and load agent, cooperate with each other to achieve the overall control goals. Particle swarm optimization (PSO) is utilized to optimize the overall system and enhance the intelligence of the integrated building and microgrid system. A Graphical User Interface (GUI) based platform is designed for customers to input their preferences and monitor the results. Two sets of case studies are carried out and corresponding simulation results are presented in this paper.

From one to many: planning for loosely coupled multi-agent systems

Abstract: Loosely coupled multi-agent systems are perceived as easier to plan for because they require less coordination between agent sub-plans. In this paper we set out to formalize this intuition. We establish an upper bound on the complexity of multi-agent planning problems that depends exponentially on two parameters quantifying the level of agents' coupling, and on these parameters only. The first parameter is problem-independent, and it measures the inherent level of coupling within the system. The second is problem-specific and it has to do with the minmax number of action-commitments per agent required to solve the problem. Most importantly, the direct dependence on the number of agents, on the overall size of the problem, and on the length of the agents' plans, is only polynomial. This result is obtained using a new algorithmic methodology which we call "planning as CSP+planning". We believe this to be one of the first formal results to both quantify the notion of agents' coupling, and to demonstrate a multi-agent planning algorithm that, for fixed coupling levels, scales polynomially with the size of the problem.

BioWar: scalable agent-based model of bioattacks

Abstract: While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.

Introductory tutorial: agent-based modeling and simulation

Abstract: Agent-based simulation (ABS) is an approach to modeling systems comprised of individual, autonomous, interacting “agents.” Agent-based modeling offers ways to more easily model individual behaviors and how behaviors affect others in ways that have not been available before. There is much interest in developing agent-based models for many application problem domains. Applications range from modeling agent behavior in supply chains and the stock market, to predicting the success of marketing campaigns and the spread of epidemics, to projecting the future needs of the healthcare system. Progress in the area suggests that ABS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use agent-based models as electronic laboratories to aid in discovery. This brief tutorial introduces agent-based modeling and simulation by describing the basic ideas of ABS, discussing some applications, and addressing methods for developing agent-based models.