• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.176-181
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• 2001

Due to the increasing complexity to handle conflicts and interruptions caused by resource failures and rush orders, shop control is obliged to redesign its organization according to the changing demands of the manufacturing control. These demands are leading to the development of decentralization and gradually to their permanent optimization. As a result, a powerful modeling method which can be adapted efficiently is required. The use of agent theory enables specific modeling of the relevant shop planning activities. The planning activities are modeled in a so-called activity modeling through the definition of three classes of agents; Plan Agent, Manufacturing System Agent and Control Agent as well as the description of the cooperative relationship among these agents. On the basis of the activity model the agent-based shop control method is developed which emphasizes the distributed problem-solving and the cooperation with relevant agents.

• Restorative Dentistry and Endodontics
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• v.45 no.2
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• pp.13.1-13.10
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• 2020

Objective: The aim of this study was to evaluate the influence of different modeling agents on the surface microhardness (Vickers hardness number; VHN), roughness (Ra), and color change (ΔE) of a nano-hybrid composite with or without exposure to discoloration by coffee. Materials and Methods: Sixty-four cylinder-shaped nano-hybrid composite specimens were prepared using a Teflon mold. The specimens' surfaces were prepared according to the following groups: group 1, no modeling agent; group 2, Modeling Liquid; group 3, a universal adhesive (G-Premio Bond); and group 4, the first step of a 2-step self-adhesive system (OptiBond XTR). Specimens were randomly allocated into 2 groups (n = 8) according to the storage medium (distilled water or coffee). VHN, Ra, and ΔE were measured at 24 hours, 1 week, and 6 weeks. The Kruskal-Wallis test followed by the Bonferroni correction for pairwise comparisons was used for statistical analysis (α = 0.05). Results: Storage time did not influence the VHN of the nano-hybrid composite in any group (p > 0.05). OptiBond XTR Primer application affected the VHN negatively in all investigated storage medium and time conditions (p < 0.05). Modeling Liquid application yielded improved Ra values for the specimens stored in coffee at each time point (p < 0.05). Modeling Liquid application was associated with the lowest ΔE values in all investigated storage medium and time conditions (p < 0.05). Conclusion: Different types of modeling agents could affect the surface properties and discoloration of nano-hybrid composites.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.241-244
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• 1996

There is a growing tendency to consider organizational learning as a mechanism for improving organizations and the rate at which organizations learn becomes perceived as a source for attaining competitive advantage. The objective of this research is to present a two-phase(learning efficient, and learning-effective) organizational modeling methodology based on the cognitive-maps and agents concept, and to describe how the result of the modeling can be used in the organizational learning context.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.113-128
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• 2000

This paper presents a first year report of an ongoing multi-year project to develop a systolic parallel simulation system for dynamic traffic assignment. The fundamental approach to the simulation is systolic parallel processing based on autonomous agent modeling. Agents continuously act on their own initiatives and access to database to get the status of the simulation world. Various agents are defined in order to populate the simulation world. In particular existing modls and algorithm were incorporated in designing the behavior of relevant agents such as car-following model headway distribution Frank-Wolf algorithm and so on. Simulation is based on predetermined routes between centroids that are computed off-line by a conventional optimal path-finding algorithm. Iterating the cycles of optimization-then-simulation the proposed system will provide a realistic and valuable traffic assignment. Gangnum-Gu district in Seoul is selected for the target are for the modeling. It is expected that realtime traffic assignment services can be provided on the internet within 3 years.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.385-402
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• 2010

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2195-2206
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• 2021

In order to mitigate the damage caused by accidents in nuclear power plants (NPPs), evacuation strategies are usually managed on the basis of off-site effects such as the diffusion of radioactive materials and evacuee traffic simulations. However, the interactive behavior between evacuees and the accident environment has a significant effect on the consequential gap. Agent-based modeling (ABM) is a method that can control and observe such interactions by establishing agents (i.e., the evacuees) and patches (i.e., the accident environments). In this paper, a radiological emergency evacuation model is constructed to realistically check the effectiveness of an evacuation strategy using NetLogo, an ABM toolbox. Geographic layers such as radiation sources, roads, buildings, and shelters were downloaded from an official geographic information system (GIS) of Korea, and were modified into respective patches. The dispersion model adopted from the puff equation was also modified to fit the patches on the geographic layer. The evacuees were defined as vehicle agents and a traffic model was implemented by combining the shortest path search (determined by an A ^* algorithm) and a traffic flow model incorporated in the Nagel-Schreckenberg cellular automata model. To evaluate the radiological harm to the evacuees due to the spread of radioactive materials, a simple exposure model was established to calculate the overlap fraction between the agents and the dispersion patches. This paper aims to demonstrate that the potential of ABM can handle disaster evacuation strategies more realistically than previous approaches.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.172-179
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• 2006

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on between-model interaction. This paper investigates the interaction and co-evolutionary process among heterogeneous artificial agents in the bargaining game. We present two kinds of the artificial agents participating in the bargaining game. They play some bargaining games with their strategies based on genetic algorithm (GA) and reinforcement learning (RL). We compare agents' performance between two agents under various conditions which are the changes of the parameters of artificial agents and the maximal number of round in the bargaining game. Finally, we discuss which agents show better performance and why the results are produced.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.2
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• pp.44-49
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• 2007

An agent is an abstract unit for problem solving in the field of distributed artificial intelligence, and an agent-based system is designed and implemented based on the definition of agent as its central concept. Agent modeling is advantageous to abstraction, disintegration and structuring for describing complex system, so its application is increased in various areas including air traffic control, power transmission, e-commerce and medicine. There is no agreed definition of agent but agents have common points as follows: autonomy, reactivity, pro-activeness and cooperation. An agent-oriented modeling is an approach of a concept different form existing object-oriented modeling. This study proposed the agent application for E-Beam manufacturing system. To evaluate the performance of the proposed process design, we used the JADE library. The JADE toolkit provides a FIPA-compliant agent platform and a package to develp Java agents. It provides a basic set of functionalities that are regarded as essential for an autonomous agent architecture.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.257-261
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• 2003

In this study, we investigated the modeling and design of an Intelligent Agent System (IAS). To achieve this goal, we introduced several kinds of agents that exhibit intelligent features. These are the main agent, management agent, watcher agent, report agent and application agent. We applied the intelligent agent concept to two different application fields, i.e. the intelligent agent system for pattern classification and the intelligent agent system for bank asset management modeling.

• 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.