• Korean System Dynamics Review
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• v.5 no.2
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• pp.175-197
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• 2004

The research studies the classic beer game simulation model from a new perspective. It does so by providing each agent with two ordering policies, and creating a set of rules that allow an agent to change its policy. Such a change is triggered based on an agent's confidence in their own performance, and on the relative confidence of their nearest neighbour. The overall effect is that policy diffusion can occur, where, under certain circumstances, an agent will mimic the behaviour of its neighbour, if it believes that its neighbour is performing better. The motivation behind this research is to provide an experimental base upon which the decision making strategies of business agent can be studied.

• Journal of Korea Game Society
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• v.19 no.6
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• pp.61-70
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• 2019

In this paper, we apply reinforcement learning in the field of management simulation game to check whether game agents achieve autonomously given goal. In this system, we apply PPO (Proximal Policy Optimization) algorithm in the Unity Machine Learning (ML) Agent environment and the game agent is designed to automatically find a way to play. Five game scenario simulation experiments were conducted to verify their usefulness. As a result, it was confirmed that the game agent achieves the goal through learning despite the change of environment variables in the game.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.670-675
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• 1994

Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.297-304
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• 2021

In a duopoly situation, one firm can gain competitive advantage by attracting the price-sensitive customers from its competitor leading to higher profits through higher sale revenue for the competing company. A simulation study in which there are two electric car manufacturers with agent based modelling was conducted in order to verify this contention. The first step consisted of defining the baseline. Simulations of 1000 times and agent-based modelling were conducted with the assumption that company 1 reduced its price to the maximum of 20% thereby contributing to the switch-over of a maximum of 40% of the price sensitive customers of company 2. The results of 1000 simulations and agent-based modelling highlighted that price reduction by company 1 resulted in a significant increase in the number of customers, presumably due to switch-over from company 2 and there was a corresponding increase in revenues from both of the sales avenues. Thus, Company 1 achieved competitive advantage by marketing its cars to the customers of Company 2 using price reduction strategy to attract them. This study has ramifications for companies that aim to sway the price sensitive customers from a competitor.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.49-55
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• 2013

Recently the CGF/SAF (Computer Generated Force / Semi-Automated Force) technology has been getting attention to deal with the increasing complexity in a DM&S(Defence Modeling and Simulation) environment. OneSAF is one of well-known CGF/SAF systems, however, it is not able to support the DEVS framework which is an advanced discrete event based modeling and simulation environment. Especially, most DM&S systems in Korea has been developed on the basis of the DEVS framework. In this paper, we have proposed the agent-based SAF design methodology and tool for supporting DEVS M&S environment. The proposed SAF modeling tool is divided into two parts; the agent modeling part and SAF modeling part. In the agent modeling environment, the modeler can simply create the agent model by writing down the necessary rules. It also provides the agent testing environment so that the modeler maybe conveniently verify the prescribed agent model. The SAF model is finally created by combing the individual agents based on the pre-defined structure. DM&S engineers will be able to employ our tools and modeling methodology to design the DEVS-based DM&S system to be developed.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.808-813
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• 2003

Maintaining agility and responsiveness m designing and manufacturing activities are the key issues for manufacturing companies to cope with global competition. Distributed design and control systems are regarded as an efficient solution for agility and responsiveness. However, distributed nature of a manufacturing system complicates production activities such as design, simulation, scheduling, and execution control. Especially, existing simulation systems have limited external integration capabilities, which make it difficult to implement complex control mechanisms for the distributed manufacturing systems. Moreover, integration and coupling of heterogeneous components and models are commonly required for the simulation of complex distributed systems. In this paper, a collaborative and adaptive simulation architecture is proposed as an open framework for simulation and analysis of the distributed manufacturing enterprises. By incorporating agents with their distributed characteristics of autonomy, intelligence, and goal-driven behavior, the proposed agent-based simulation architecture can be easily adapted to support the agile and distributed manufacturing systems. The architecture supports the coordination and cooperation relations, and provides a communication middleware among the participants in simulation.

• 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 Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.848-854
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• 1999

In this paper, we designed and implemented a precise pulse motor controller chip that generates the pulse needed to control step motor, DC servo and AC servo motors. This chip generates maximum pulse output rate of 5Mpps and has the quasi-S driving capability and speed and moving distance override capability during driving. We designed this chip with VHDL and executed a logic simulation and synthesis using Synopsys tool. The pre-layout simulation and post-layout simulation was executed by Compass tool. This chip was produced with 100 pins, PQFP package by 0.8${\mu}{\textrm}{m}$ gate array process and implemented by completely digital logic. We developed the test hardware board of performance and the CAMC(Computer Aided Motor Controller) Agent softwate to test the performance of the pulse motor controller chip produced. CAMC Agent enables user to set parameters needed to control motor with easy GUI(Graphic User Interface) environment and to display the output response of motor graphically.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.91-100
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• 2003

In this paper we investigate the role that warfare played In the formation of the network of alliances between sites that are associated with the formation of the state in the Valley of Oaxaca, Mexico. A model of state formation proposed by Marcos and Flannery (1996) is used as the basis for an agent-based simulation model. Agents reside in sites and their actions are constrained by knowledge extracted from the Oaxaca Surface Archaeological Survey (Kowalewski 1989). The simulation is run with two different sets of constraint rules for the agents. The first set is based upon the raw data collected in the surface survey. This represents a total of 79 sites and constitutes a minimal level of warfare (raiding) in the Valley. The other site represents the generalization of these constraints to sites with similar locational characteristics. This set corresponds to 987 sites and represents a much more active role for warfare in the Valley. The rules were produced by a data mining technique, Decision Trees, guided by Genetic Algorithms. Simulations were run using the two different rule sets and compared with each other and the archaeological data for the Valley. The results strongly suggest that warfare was a necessary process in the aggregations of resources needed to support the emergence of the state in the Valley.