• 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.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.1-17
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• 2005

Agent technology has been widely applied in today's electronic business, such as mobile agents, multi-agent information systems, etc. In particular, multi-agent systems have been applied as powerful simulation tools to study complex business networks composed of various self-interested trading firms and/or human beings. In this paper, we build an integrated model that consists of a multi-agent B2C market model and a B2B trade network model, and incorporate more reality than much of prior work. Then with this model, we carry out experimental studies on two different strategies that are common in electronic business - 'loyal' strategy (retailers try to build stable cooperation with suppiers to ensure material supply) and 'cost-saving' strategy (retailers try to reduce cost by choosing suppliers with lower wholesale price).

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.489-497
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• 2001

This paper describe design and implementation of a medical multi-agent system platform called MAMI (Multi-Agent system for Medical Image), which provides intelligent medical information services. The most important component of MAMI is a medical multi-agent system platform that supports a physical environment that medical agents can be deployed. MAMI follows FIPA (Foundation for Intelligent Physical Agent)\`s agent management reference model. In MAMI, COM(Common Object Model) and XML (eXtensibel Markup Language) for encoding ACL (Agent Communication Language) are used for multi-agent communications. In MAMI, a medical staff is conceptualized as an agent and integrated with multi-agent systems. MAMI agent platform provides an infrastructure applicable to share necessary knowledge between human agents and software agents. So MAMI makes intelligent medical information services easier.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.8
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• pp.573-580
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• 2020

One of challengeable problems of multi-agent systems is a positioning control. Augmented Cucker-Smale model is using for controlling position and velocity of the multi-agent system. The original model applies same coefficients to all agents in same group, so that does not consider characteristic of each agent. To enhance performance of the original model, this paper transforms original coefficients to Mahalanobis distance coefficients that reflects an initial distribution of multi-agent systems and applies statistical degrees of freedom. This paper not only confirms tendency of enhanced performance of the suggested model by using monte-carlo simulation, but also additionally compares trajectory of the original model with the suggested model to confirm coefficients of Mahalanobis distance performing correctly.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.537-542
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• 2008

The protection against transient instability and consequent out-of-step condition is a major concern for the utility industry. Unstable system may cause serious damage to system elements such as generators and transmission lines. Therefore, out-of-step detection is essential to operate a system safely. Also, a multi-agent system is one that consists of a number of agents, which interact with one another. Multi-agent systems(MAS) can offer the flexibility and the adaptability to the previous algorithm. In this paper, the detection algorithm of out-of-step is designed by multi-agent system and implemented by EMTP-MODELS. To verify performance of the proposed algorithm based on multi-agent system, simulations by EMTP have been carried out.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.587-594
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• 2001

Multi-agent system fits to the distributed and open internet environments. In a multi-agent system, agents must cooperate with each other through a coordination procedure, when the conflicts between agents arise. Where those are caused by the point that each action acts for a purpose separately without coordination. But previous researches for coordination methods in multi-agent system have a deficiency that they cannot solve correctly the cooperation problem between agents, which have different goals in dynamic environment. In this paper, we suggest the automatic coordination model for multi-agent system using neural network and reinforcement learning in dynamic environment. We have competitive experiment between multi-agents that have complexity environment and diverse activity. And we analysis and evaluate effect of activity of multi-agents. The results show that the proposed method is proper.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2072-2077
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• 2007

The primary objective of all power systems is to maintain the reliability and to minimize outage time for fault or the others. The frequency relaying algorithm perceives a variation of system frequency and thereafter detects the unbalance between generation and load. A multi-agent system is composed of multiple interacting computing elements that are known as agents. In this paper, frequency relaying algorithm is designed by multi-agent system and is implemented by EMTP-MODELS. To verify performance of the frequency relaying algorithm based on multi-agent system, simulations by EMTP have been carried out.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.49-57
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• 2008

In this paper, we suggested a methodology that can be used by an agent to learn models of other agents in a multi-agent system. To construct these model, we used influence diagram as a modeling tool. We present a method for learning models of the other agents at the decision nodes, value nodes, and chance nodes in influence diagram. We concentrated on learning of the other agents at the value node by using neural network learning technique. Furthermore, we treated anti-air units in anti-air defense domain as agents in multi. agent system.