• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.118-136
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• 1997

The multi-agent system is an environment in which applications solve tasks by cooperating with each other. In the multi-agent system, a user can solve complex problems by issuing a single command instead of a series of commands. However, agents depend on the state of other agents for they cooperate with each other. The problem worsens as multi-agent systems are connected to from a group of multi-agent systems. In this paper, we present the termination problem of an agent in a multi-agent system or a group of multi-agent systems. We also give asolution of the problem and present an example through implemented multi-agent systems.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.05a
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• pp.1-20
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• 2003

In this research, we developed a Multi-Agent based Negotiation Support System to be able to increase the competitive power of a company in dynamic environment and correspond to various orders of customers by diffusion of electronic commerce. The system uses the agent technology that is being embossed as new paradigm in dynamic environment and flexible system framework. The multi-agent technology is used to solve these problem through cooperation of agent. The system consists of six sub agents: mediator, manufacturability analysis agent, process planning agent, scheduling agent, selection agent, negotiation-strategy building agent. In this paper, the proposed Multi-Agent based Negotiation Support System takes aim at the automation of transaction process from ordering to manufacturing plan through the automation of negotiation that is the most important in order-taking transaction.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.617-621
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• 2012

This paper studies asymptotic consensus problem for linear multi-agent systems. We propose a distributed state feedback control algorithm for solving the problem under fixed and undirected network communication. In contrast with the conventional algorithms that use global information (e.g., graph connectivity), the proposed algorithm only uses local information from neighbors. The principle for achieving asymptotic consensus is that, for each agent, a distributed update law gradually increases the coupling gain of LQR-type feedback and thus, the overall stability of the multi-agent system is recovered by the gain margin of LQR.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.712-717
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• 2007

Using home network system including home server, home service robot, a variety of device, it is generally known that application of Multi Agent System for performing variously distributed process that can be occur in home environment, is efficient method. In this system, it is intelligent service robot that a key of human interface and physical service offer Therefore, using application of established multi agent system, we can defined Multi Agent Robot System. In 'open' home environment, between all agent data interaction and cooperation are needed for Multi Agent System offer to user that more efficient service. For this, we focus our attention on define as agent that can autonomic drive and offer to user that physical service robots and, design, suggest the simulator can display that between robot agents communication or between other agents, like home server, and robot agents communication information to user interface.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.277-282
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• 2004

This paper outlines our approach and the underlying design principles aimed at the generation of a theory of coordination. Such theory would assist in designing new Multi-Agent Systems(MAS) and provide trouble-shooting tools for suboptimally functioning MAS. This paper also describes the decisions that have been made in this endeavor. We have been able to show via a simplified model that approach is feasible and can produce results.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.508-512
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• 2016

This paper discusses a Takagi-Sugeno (T-S) fuzzy controller design problem for a phugoid model-based multi-agent system. The error between the state of a phugoid model and a reference is defined to construct a multi-agent system model. A T-S fuzzy model of the multi-agent system is built by introducing a nonlinear controller. A fuzzy controller is then designed to stabilize the T-S fuzzy model, where the synthesis condition is represented in terms of linear matrix inequalities.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.91-103
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• 2004

Multi-agent systems for semantic web applications require efficient implementation of agent architectures without sacrificing the flexibility and the level of abstraction that agent architectures provide. In this paper, we present an agent system, called VivAce, which is implemented in Java to achieve both high efficiency and the level of abstraction provided by the BDI agent architecture. VivAce (Vivid Agent Computing Environment) has the characteristics of a vivid agent through the BDI agent model. A vivid agent is a software-controlled system whose state comprises the mental components of knowledge, perceptions, tasks, and intentions, and whose behavior is represented by means of action and reaction rules. We first identify the requirements for multi-agent systems and then present the relevant features of VivAce and experimental results.

• Asia pacific journal of information systems
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• v.14 no.4
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• pp.49-70
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• 2004

Multi-agent technology is being regarded as one of the promising technologies for today's supply chain management because of its desirable features such as autonomy, intelligence, and collaboration. This paper suggests a multi-agent system architecture with which companies can improve the efficiency of their supply chains by collaborative operation. Reflecting the practical difficulties of collaboration in complex supply chains, the architecture allows agent systems to share information with only neighboring companies for the coordinated operation. The suggested architecture is elaborated with a collaboration model based on Petri-net, conversation models for communication, and internal behavior models of each agent. A simulation experiment was performed for the evaluation of the suggested architecture. The result implies that when the estimation of market demand is higher than a certain level, the suggested architecture can be beneficial.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.123-133
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• 2001

Medical domain is very applicable for multi-agent system because medical information systems need much knowledge and close relationship with medical staff, In this paper, we describe design and implementation of an intelligent medical multi-agent platform that provides medical images'information services. This platform supports a physical environment that medical agents can be deployed following FIPA(Foundation for Intelligent Physical Agent)\`s agent management reference model. To use a variety of components on Windows, COM(Common Object Model) interfaces and XML(extensible Markup Language) for encoding ACL(Agent Communication Language) are used for multi-agent communications. Since many kinds of diverse and close relationships with medical staff) are essential, a medical staff is conceptualized as an agent and integrated with multi-agent systems. Also it provides an infrastructure applicable to share necessary knowledge between human agents and software agents in order to make intelligent medical information services easier.