• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.43-49
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• 2000

The World Wide Web has become a vast information resource where virtually any information can be found. There is a pressing need for appropriate Web search tools due to the very vastness of information space, the rate of growth, and the volatility of data. The agent technology has been studied to address these issues and led to the creation of an adaptive, Proactive. personalized search tools. This study looks into the mechanism of collaborative filtering of information and suggests a decentralized collaborating agents model for information discovery.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.204-207
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• 2020

커피는 전 세계인들의 꾸준한 인기를 받고 있으며, 커피머신에 대한 관심이 증가하고 있다. 따라서 본 논문의 커피머신은 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망을 이용하여 지도학습 및 비지도 학습으로 개인에게 최적화된 커피를 제공한다. 또한, 사용자는 어플리케이션을 통해 커피머신을 무선으로 조작할 수 있고 웹을 통한 관리자 모드로 데이터를 관리하고 학습시킬 수 있다.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.399-406
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• 2002

The goal of this research has been to develop an adaptive user agent for web surfing. To achieve this goal, the research has concentrated on three issues: collection of user data, construction and improvement of user profile, and adaptation by applying the user profile. The main outcome from the research is a prototype system that provides the functional definition and componential design scheme for an adaptive user agent for the web environment. Internally, the system achieves its operational goal from the cooperation of two independent agents. They are IIA (Interactive Interface Agent) and UPA (User Profiling Agent). As a tool for providing a user-friendly interface environment, the IIA employs the Keyword Index, which is a list of index terms of a webpage as well as a keyword menu for subsequent queries, and the Suggest Link, which is a hierarchical list of URLs showing the past browsing procedure of the user. The UPA reflects in the User Profile, both the static and the dynamic information obtained from the user's browsing behavior. In particular, a user's interests are represented in the form of Interest Vectors which, based on the similarity of the vectors, is subject to update and creation, thus dynamically profiling the user's ever-shifting interests.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.567-584
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• 2013

This study aims to explore an evolutionary path of the IT service industry in Fairfax County using the Cluster Adaptive Cycle model in economic geography. The analysis is based on detailed historical and industrial information obtained through a variety of data sources including local archival materials, economic census, and interviews. This study also performs a shift-share analysis during the period of 1990 to 2011. Using the adaptive cycle model, the local IT service industry is indicated by a trajectory of constant cluster mutation. The evolution of the local IT service industry has been closely related to federal government policy due to the regional specificity of the National Capital Region and the proximity of the Department of Defense. Although the economic downturn of the late 2000s, the local IT service industry has been notable resilience and adapted to a changing market and technological environment. This constant mutation of the local industry is resulted from not only high resilience which is based on the large government procurement market, the reinforcement of adaptive capacity of the local firms and the network of economic agents such as firm and supporting institutions, but also high flexibility of the knowledge-based service industry to a changing business environment.

• Journal of KIISE
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• v.42 no.12
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• pp.1503-1521
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• 2015

Agent-based modeling and simulation (ABMS) is used to model of market and social phenomena by utilizing agents' fine-grained behaviors and interactions that cannot be implemented in a conventional simulation. However, ABMS represents irrational agents and hinders the achievement of individual or overall goals since ABMS is based on agent-based software, which follows the principle of rationality at the knowledge level [1]. This problem was solved in the agent-based software engineering (ABSE) field by using behavior laws for the social level [2]. However, they still do not propose the specific development methodology for how to develop the social level in a systematic way. Therefore, in order to propose agent-based modeling and simulation methods that reflect the behavior laws of social level characteristics, our study used the Tropos that can combine ABSE and social behavior laws for the presentation of concrete tasks and deliverables for each development step by step. In addition, the proposed method will be specified through experiments with specific application examples and case studies on the self-adaptive smart grid and the military domain system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1177-1188
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• 2004

Multi-agent systems have been developed for supporting intelligent collaboration of distributed and independent software entities and are be-ing widely used for various applications. For the collaboration among agents, conversation policies (or interaction protocols) mutually agreed by agents are used. In today's dynamic electronic market environment, there can be frequent changes in conversation policies induced by the changes in transaction methods in the market, and thus, the importance of ad hoc conversation policies is increasing. In existing agent platforms, they allow the use of only several standard or fixed conversation policies, which requires inevitable re implementation for ad hoc conversation policies and leads to inefficiency and intricacy. This paper designs an agent platform that supports ad hoc conversation policies and presents the prototype implementation. The suggested system includes an exchangeable and interpretable conversation policy model, a meta conversation procedure for exchanging new conversation policies, and a mechanism for performing actual transactions with exchanged conversation policies in run time in an adaptive way.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• Journal of KIISE:Information Networking
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• v.32 no.5
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• pp.625-632
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• 2005

Recently, real time services have been demanding a lot and the number of mobile devices is increasing extremely. Many researchers are focusing on decreasing handoff or signaling cost, produced when mobile devices are moving around. With these efforts, HMIPv6(Hierarchical Mobile Internet Protocol Version 6) was proposed. Mobile nodes do not need to register their locations to Home Agents whenever crossing over subnets within a MAP domain. In HMIPv6, mobile nodes choose the farthest MAP without considering node mobility pattern. However, a large MAP domain is not always efficient for a slow moving node and required additional work to choose a MAP in HMIPv6. Hence, this paper proposes 'Performance Analysis of HMIPv6 applying adaptive MAP Domain Site'.