• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.1-21
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• 2003

As electronic commerce(EC) has grown rapidly, agents that work on the behalf of humans on the Internet are being used actively. However, most of the EC agents have some problems. They fail to fully support buyers' decision making behaviors and sellers' information supply activities. Further, they are not suited for mobile computing environment. In this paper, we introduce a Multi-Mobile Agents System(MMAS) that has been developed according to a conceptual framework that corrects the aforementioned problems. Built by using Tokyo IBM ASDK(Aglets Software Development Kit) for the area of hotel reservation, the system consists of buyer- and seller-side agents that interact with each other; buyer-side agents help buyers to make purchasing decisions by collecting and analyzing information through applying a multi-criteria decision making method, while seller-side agents substitute fur sellers by managing databases and providing real-time information to the buyer-side agents. In this system, multiple agents perform their shared tasks at the same time in order to increase efficiency. Users do not have to keep the connection with the system because they are notified when tasks are done.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.73-77
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• 2001

In this paper, we propose a virtual action system based on information agents, We call the system the MultiHammer, MultiHammer can be used for studying and analyzing online actions. MuiltiHammer provides functions of implement-ing a meta online action site and an experiment environ-ment. We have been using MultiHammer as an experiment as an experiment environment for BiddinBot. BiddingBot aims at assisting users to bid simultaneously in multiple online auctions. In order to bid simultaneously in multiple online auctions. In order to analyze the behavior of BiddngBot, we need to pur-chase a lot of items. It is hard for us to prepare a lot of fund to show usability and advantage of BiddingBot. MultiHam-mer enables us to effectively analyze the behavior of BiddingBot. MultiHammer consists of three types of agents for information collecting data storing and auctioning. Agents for information wrappers. To make agent work as wrarp-pers, we heed to realize software modules for each online action site. Implementing these modules reguires a lot of time and patience. To address this problem, we designed a support mechanism for developing the modules. Agents for data storing record the data gathered by agents for informa-tion collecting. Agents for auctioning provide online services using data recorded by agents for data storing. By recording the activities in auction sites. MultiHammer can recreate any situation and trace auction for experimentation, Users can participate in virtual using the same information in real online auctions. Users also participate in real auc-tions via wrapper agents for information collecting

• 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.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.41-53
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• 2000

The purpose of the paper is communication between multi-agents for student's learning at web based collaborative learning. First, this study investigated the whole contents and characteristics of an agent system and discussed KQML, communication language between multi-agents. Also, we suggested architecture of an agent based system for collaborative learning and interaction method between agents using KQML. We design어 and implemented collaborative learning system using Java programming language, and we also demonstrate the efficiency of collaborative learning system by communication between multi-agents through experiments.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1173-1176
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• 2002

Interaction builds up among agents in order to accomplish their goals. Argumentation is important for agent negotiation and interaction. In this paper, we discuss about the framework for multi-agent argumentation and the way multi-agents co-operates between each other. We identify aspects of classical argumentation theory that are suitable and useful for artificial agents.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3477-3489
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• 1999

Our study is focused on a multi-agent system which provides the efficient teleconferencing facility with the help of the intelligent agents who allow the participants to minimize the effort to call meeting. Our system has multi-agent architecture and use the KQML to communicate among agents. Communicating through the KQML allows us to overcome the heterogeneity when we add new agents into the system. In this paper, we propose a multi-agent structure for the teleconferencing system based on the web and describe in detail the now or the KQML messages among agents.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.155-169
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• 2003

A multi-agent technology has emerged as new paradigms that can flexibly and promptly cope with various environmental changes and complex problems. Accordingly, many studies have been carried out to establish multi-agent systems in an effort to solve dynamic problems in many fields. However, most previous research on the multi-agent frameworks aimed at, on the behalf of a user, exchanging and sharing information among agents, reusing agents, and suggesting job cooperation in order to integrate and assimilate heterogeneous agents. That is, their frameworks mainly focused on the basic functions of general multi-agents. Therefore, they are not suitable to the development of the proper system for a specific field such as a negotiation. The goal of this research is to design a multi-agent framework for the negotiation system that supports the evaluation of the negotiation messages, management of the negotiation messages, and message exchanges among the negotiation agents.

• 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.

• 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.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.965-968
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• 2000

We develop the Multi-Mobile-agents system for realizing effective cooperative task processing in the network environment. In this system, agents are separated / fused by the Place and migrated to another computer. A Place can assign agents to other places by agents migration to be flat the time to execute agents’ action. In this paper, the effectiveness of this system is shown by experimental results applying an agent given simple task.