• Korean System Dynamics Review
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• v.6 no.1
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• pp.33-70
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• 2005

This study explores the process of corporate corruption via a percolation-based system dynamics model. The preliminary model is an agent-based model constructed in the terms of the corruption networking between ego and other. In the model, the agents behave depending on percolation rules, which represent (1) passing on the corruption opportunities and (2) accepting it. To describe the percolation process in the networks, we develop a further complicated model by combining the basic model with the Bethe lattice. Through the complicated model, we suggest (1) the dynamics of the systemic corporate corruption, (2) 4 patterns of the corruption, and (3) the institutionalization of the corruption. These simulation results provide theoretical and practical implications.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

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 choose 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-output 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 these algorithms 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. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8B
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• pp.716-729
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• 2003

As the public Internet and private internet have grown from small networks into large infrastructures, the need to more systematically manage the large number of network components within these networks has grown more important as well. The rapid growth of network size has brought into question the salability of the existing centralized model, such as SNMP(Simple Network Management Protocol) and CMIP(Common Management Information Protocol). Thus, for efficient network management, researches about mobile agent have also been performed recently. This paper presents analytical models of centralized approach based on SNMP protocol, distributed approach based on mobile agent, and mixed mode to make up for shortcomings of SNMP and mobile agent. We compare the performance of these analytical models based on network management response time. Experiment results show that performance of mobile agent and the nixed mode is less sensitive to the delay in WAN network environment. However, SNMP is more efficient for the simple network environment like LAN. We also propose an adaptive network management algorithm in consideration of network t environment. delay, task, and the number of nodes based on the results of analytical models. The results show that the adaptive network management algorithm can reduce the network management response time by 10% compared with either mobile agent or mixed mode network management algorithm.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.447-447
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• 2000

This paper proposes an Object-oriented model for Generic Equipment Model(GEM) which is applied to semiconductor processing Equipment. A proposed implementation model for GEM consists of three modules : a module to convert SECS-II message and a module to transmit messages between GEM and a physical equipment. The proposed Object-oriented model for GEM has advantages in reuse and user management from the point of view of development program. Especially, this paper proposes a GEM agent COM, which is a base of GEM, transmits messages and events among modules. This Object-oriented model is analysed and developed by using UML.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.75-89
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• 2000

This paper deals with complex system theory to describe guerrilla warfare situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. EINSTein model is an agent-based artificial "laboratory" for exploring self-organized emergent behavior in land combat. Many studies have shown that existing Lanchester equations used in most guerrilla warfare models do not describe changes of combat units, real guerrilla warfare situation and qualitative factors in combat. Future warfare will be information warfare with various weapon system and complex combat units. We have compared and tested results with Lanchester models and EINSTein model. And the EINSTein model has been applied and analyzed to guerrilla warfare model (C4I facilities, coastal, urbanized terrain critical facilities defense). The results show that the EINSTein model has a possibility to apply and analyze guerrilla warfare more properly than Lanchester models.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.96-104
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• 2017

As new smart devices are evolved into the intelligent agent who can reflect user intention and use context, user experience design for easy and convenient usability becomes a core competitive edge. Under the assumption that human centered natural interaction is necessary for the optimal smart TV experience, this study explores the types of voice interaction which are peculiar to TV watching context. In order to build a model for the users to naturally interact with Smart TV, conversation patterns were collected by requesting key features of Smart TV to intelligent agent. Collected sentences were applied to CfA model and classified by responses to activate features. The classified conversation patterns were divided into feature activation and information search. This study has identified that CfC1 occurred when voice interaction between Smart TV and users was vague and CfC2 occurred when the requests were complex or conditional. In conclusion, Simple Request Type is the most efficient model and voice interaction is more appropriate to use to clarify users' vague requests.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.2
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• pp.262-274
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• 2013

Heavy rain and heavy snow as representative extreme weather are recently an issue in urban area. The paper aims at modeling the scenarios of evacuation that minimizes economic loss of the designated urban area with improving travel efficiency by providing road closure information facing an extremely heavy rainfall. The paper develops a model by using a NetLogo toolkit applied to the study area of Seocho-dong, Seocho-gu, Seoul. The model conducts a simulation of travel time under different scenarios of information provision. The simulation results show that it is efficient to provide the information of road closure to 20~40% of the drivers under the scenario of humid road or rainfall less than 20mm, whereas to 40~60% of the drivers under the scenario of heavy rainfall more than 20mm.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.68-78
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• 2019

Recently, housing prices have surged, and the government has implemented various regulations, such as finance and taxes. Because of the policy, the nationwide housing price have stabilized, but polarization has occurred. Some argue that regulation can adversely affect the actual demand. Therefore, not only the correlation between market variables but also ripple effect of policy has to be analyzed in policy planning and analysis from a microscopic point of view. In this study, a simulation model was developed by integrating system dynamics for analyzing market structure and agent-based model for modeling decision process of market participants. This research applied the financial regulation and the tax regulation to the model and evaluated the policy effectiveness. This study reveals which feedback dominates according to the policies, which have same purpose. It is because market participants make different decision for each policy. Furthermore, there were other ripple effects not only in the policy target submarket but also in other submarket.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.815-818
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• 2007

As wireless communications and mobile multimedia services are booming nowadays, systematic research of the overall aspects of mobile security is crucial. This paper presents a frame model for guising the systematic investigation of mobile security. Based on the introduction of some background viewpoints of security targets from n novel perspective, the kamework is described as a hierarchical model in which mobile security research is partitioned into three different layers.