• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.333-338
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• 2006

최근 사용자에 대한 많은 정보를 얻는 것이 가능해지면서, 데이터마이닝 기법이나 Contents 추천 기법을 이용한 맞춤형 서비스가 가능하게 되었다. 특히, 대부분의 사람들에게 TV 프로그램 시청은 여가생활시간에서 가장 높은 비중을 차지 하고 있다. 따라서, 보다 지능적인 TV 프로그램 서비스를 제공하는 기술에 대하여 관심이 고조되고 있다. 본 논문에서는 TV-Anytime을 이용하여 개인화된 Electronic Program Guide (EPG)를 생성하고, 개인화된 EPG 정보를 활용하여 시청자에게 맞춤형 TV 프로그램 서비스를 제공하는 시스템에 대한 연구 결과를 제시한다. 또한 시청자의 시청패턴과 TV 프로그램 선호도를 바탕으로 시청자가 원하는 프로그램을 추천하는 TV Program Recommender Agent와 방송 및 TV 프로그램에 대한 대화를 담당하는 TV Program Helper Agent, 시스템 조정 및 메시지 전달을 담당하는 Coordinator Agent로 이루어진 멀티에이전트 기반 시스템 구조를 제시한다.

• Journal of the Korean institute of surface engineering
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• v.43 no.2
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• pp.111-120
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• 2010

In this study, the effects of multi-complex agents addition on characteristics of electroless Ni plating solution are investigated. The species and the concentration of complexing agents are major factors to control the deposition rate, P concentration, and surface morphology of plating films. Adipic acid increases the deposition rate in regardless of single- or mutli-complex agent addition. However, lactic acid effectively increases the deposition rate in case of multi-addition as the complex agents with adipic or sodium succinate acid. In addition, sodium citric acid and malic acid show good stabilizing effects of plating solution and lowering the deposition rate, because they have high complexibility. Therefore, it is suggested that the development of Ni-P plating solution suitable for diverse usages can be carried out systematically using the database from this study.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.52-53
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• 2006

현재 국내 배전자동화 시스템의 고장구간을 최소한으로 하는 불필요 정전구간 복구시스템은 고장발생 시 중앙에서 고장구간을 판단하고 Fuzzy Rule을 이용한 복구해를 통하여 계통의 Device들을 운영자가 직접 제어하는 방식이다. 이러한 방식은 원격으로 제어가 가능하며 운영자가 직접 상황에 따라 적절한 판단을 하여 신뢰성이 크다는 장점이 있지만, 운영자의 실수에 의해서 계통에 문제가 발생할 가능성도 있고, 정전복구시간 면에서 볼 때 오퍼레이터의 실수에 의해 생기는 복구지연시간은 소비자들에게는 경제적 손실 및 불편을 초래할 수 있다. 따라서 이러한 운영자의 실수를 미연에 방지하고 정전복구 시간 또한 가급적 줄일 수 있는 방안을 마련하는 것이 필요하다. 본 연구에서 제안하는 Multi-Agent 기반의 분산형 정전복구 시스템은 중앙의 제어를 통하지 않고 계통 내의 Device들이 Agent 개념의 지능을 가지고 상호 정보교환을 통하여 고장구간을 판단하고 정전을 복구하는 시스템으로 현재 배전자동화 시스템의 복구시간을 1분 이내로 단축시킬 수 있는 시스템을 제안하였다. 간단한 배전계통을 구성하고 전력계통의 현재 통신망중 유/무선의 대표적인 LAN 및 CDMA 통신을 이용한 사례연구를 통해 제안한 시스템에서의 정전구간 1분 이내의 복구시간을 입증하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.37-46
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• 2012

The scheduling problem of large products like ships, airplanes, space shuttles, assembled constructions, and automobiles is very complex in nature. To reduce inherent computational complexity, we often design scheduling systems that the original problem is decomposed into small sub-problems, which are scheduled independently and integrated into the original one. Moreover, the steep growth of communication technology and logistics makes it possible to produce a lot of multi-nation corporation by which products are produced across more than one plant. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. In this research, we suggest an agent-based coordinating mechanism for multi-level scheduling systems in supply chain. For design of a general coordination mechanism, at first, we propose a grammar to define individual scheduling agents which are responsible to their own plants, and a meta-level coordination agent which is engaged to supervise individual scheduling agents. Second, we suggest scheduling agent communication protocols for each scheduling agent topology which is classified according to the system architecture, existence of coordinator, and direction of coordination. We also suggest a scheduling agent communication language which consists of three layers : Agent Communication Layer, Scheduling Coordination Layer, Industry-specific Layer. Finally, in order to improve the efficiency of communication among scheduling agents we suggest a rough capacity coordination model which supports to monitor participating agents and analyze the status of them. With this coordination mechanism, we can easily model coordination processes of multiple scheduling systems. In the future, we will apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly-plant-scheduling agents, and a meta-level coordination agent. A series of experiment using the real-world data will be performed to examine this mechanism.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.1-8
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• 2009

Ubiquitous computing requires an intelligent environment where the users do not need to be involved in the operation. The agent technology allows effective implementation of ubiquitous system, while agent platform provides efficient and stable interaction between the agents. In this paper we propose a hierarchical P2P networking approach where the messages are combined inside the agent platform and then compressed in two levels. The proposed approach capitalizes the static nature of the messages of typical context-aware applications. Experiment with an actual multi-agent system reveals that the response time and throughput are significantly improved compared to the existing scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.572-575
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• 2004

Since the 1990s, the advancement of semiconductor technology has resulted in the development of microprocessor technology, auxiliary computer technology, and application technology such as intelligent algorithms (neural network, fuzzy, etc.). These based the development of intelligent machines. An agent is autonomous software that recognizes environment, exchanges knowledge with other agents and makes decisions. We designed agent-based sensor structure. For the purpose, first, it modeled the function of an intelligent machine. Second, it designed sensory function on the agent level.