• 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.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.139-146
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• 2011

This paper presents a new modified Contract Net Protocol (CNP) for microgrid operation based on multiagent system. The CNP is a widely used protocol for interactions among distributed problem solving. The Contract Net Interaction Protocol of the Foundation for Intelligent Physical Agents (FIPA-CNIP) is a minor modification of the original CNP for multiagent system applications. In this paper, a modified CNP (MCNP) based on the FIPA-CNIP is proposed for more specialized interactions among agents for microgrid operation. A multiagent system is designed and constructed for microgrid operation. A microgrid operation based on the multiagent system is tested to check the functionality of the proposed MCNP.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.337-341
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• 2000

In recent years, electronic commerce (EC) business has shown a tremendous growth, which put additional cognitive and time-consuming loads on the human users. Intelligent software agents are one key technology to support users doing real EC-business.We propose an Agent-based Business-to-Business(B2B) Marketplace on Internet, where constituent members cooperate or compete in order to get the best possible deals, with the aim of reducing user loads.In case of B2B deals, the good itself shows a complicated behaviour, e.g.price discounts depending o the lot, discounts for cross-buying and so on. It is impractical to concentrate all the knowledge about the goods and the trading strategies at the buyer/seller side. Instead, we include such information in the good itself, which allow us to model the goods as being agents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1294-1301
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• 2005

In this paper, we investigate the problems of existing solutions for intrusion detection and propose an agent network based on stationary and mobile agents on agent system to solve them. The proposed agent network can detect intrusions, collect their information and execute active responses against intruders by introducing various stationary and mobile agents. It will show a new approach of active responses against more intelligent and distributed intrusions.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.253-255
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• 2003

We address an integrated multimedia framework based on a software agent platform for context-aware multimedia computing. We adopt the FIPA (Foundation for Intelligent Physical Agents) platform which provides agent communications and management mechanisms. In order to express context information, we use MPEG-21 metadata for describing user characteristics and usage environment. We encapsulate such context information as a FIPA message to be delivered between agents. Based on context information, appropriate multimedia content delivery becomes possible. We present our system implementation with a use case scenario and show that our proposed framework is effective for context-aware multimedia computing so that personalization of multimedia consumption can be possible.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2618-2629
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• 1999

In this paper, we describe the design and implementation of the FDRA(Fault Detection Recovery based on Agent) running on distributed multimedia environment. DOORAE is a good example for distributed multimedia and multimedia distance education system among students and teachers during lecture. It has primitive service agents. Service functions are implemented with objected oriented concept. FDRA is a multi-agent system. It has been environment, intelligent agents interact with each other, either collaboratively or non-collaboratively, to achieve their goals. The main idea is to detect an error by using polling method. This system detects an error by polling periodically the process with relation to session. And, it is to classify the type of error s automatically by using learning rules. The merit of this system is to use the same method to recovery it as it creates a session. FDRA is a system that is able to detect an error, to classify an error type, and to recover automatically a software error based on distributed multimedia environment.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.522-527
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• 2008

Socially Intelligent agent is the agent not only having the ability to recognize and to process human affect through learning and adaptation, but also having human-like social intelligence. By making human feel familiar with the computer, the agent is expected to enhance human-computer interaction (HCI) by providing users with the personalized services and interfaces. This paper proposes the framework for socially intelligent agents behaving socially according to the emotions recognized by ID3 algorithm and psychological OCC model. Also, the agent could process with the emotion to make socially intelligent response through three layered affect functioning model. Finally, the proposed agent can be applied for the development and application of socially intelligent agent in wide areas as the agent framework having similar affect and cognitive structure with human being.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.166-169
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• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.