• 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.

• Journal of Korea Game Society
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• v.20 no.5
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• pp.65-76
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• 2020

This paper studied cooperation and reciprocity mechanism by analyzing game mechanics of MMORPG 'World of Warcraft: Classic'. MMORPG is the genre of game where social interaction vigorously occurs. This study figured out the structure of cooperation based on evolutionary psychology, anthropology and social psychology through participatory observation. Four cooperation and reciprocity mechanism were drawn through investigating game mechanics like instance dungeon, clear role setting, item, soulbind. In addition, this paper suggests providing ideas of making cooperation mechanism in the field of game design.

• Journal of Power Electronics
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• v.12 no.1
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• pp.104-112
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• 2012

In this paper a new method for the design of a simple PI controller is presented and it has been applied in the control of a Boost based PFC rectifier. The Strength Pareto evolutionary algorithm, which is based on the Pareto Optimality concept, used in Game theory literature is implemented as a multi-objective optimization approach to gain a good transient response and a high quality input current. In the proposed method, the input current harmonics and the dynamic response have been assumed as objective functions, while the PI controller's gains of the PFC rectifier (Kpi, Tpi) are design variables. The proposed algorithm generates a set of optimal gains called a Pareto Set corresponding to a Pareto Front, which is a set of optimal results for the objective functions. All of the Pareto Front points are optimum, but according to the design priority objective function, each one can be selected. Simulation and experimental results are presented to prove the superiority of the proposed design methodology over other methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.690-693
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• 2003

The purpose of two player game is that a player beats an enemy. In order to win to various enemies, a learning of various strategies is indispensable. However, the optimal action to overcome the enemies will change when the game done over and again because the enemy's actions also change dynamically. Sol it is din-cult that the player aquires the optimal action and that the specific player keeps winning to various enemies. Species who have a competition relation and affect other's existence is called a coevolution. Coevolution has recently attracred considerable interest in the community of Artificial Life and Evolutionary Computation(1). In this paper, we apply Classifier System for agent team to two player game. A reward and a penalty are given to the used rules when the agent achieve specific action in the game and each team's rulebase are evaluated based on the ranking in the league. We show that all teams can acquire the optimal actions by coevolution.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.57-66
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• 2009

A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.292-300
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• 2020

According to the development of artificial intelligence technology, attempts have been made to interpret phenomena in various fields of the real world such as economic, social, and scientific fields through computer simulations using virtual artificial agents. In the existing artificial agent-based bargaining game analysis, there was a problem that did not reflect the cost incurred when the stage progresses in the real-world bargaining game and the depreciation of the bargaining target over time. This study intends to observe the effect on the bargaining game by adding the cost incurred in the bargaining stage and depreciation of the bargaining target over time (bargaining cost) to the previous artificial agent-based bargaining game model. As a result of the experiment, it was observed that as the cost incurred in the bargaining stage increased, the two artificial agents participating in the game had a share close to half the ratio and tried to conclude the negotiation in the early stage.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.4
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• pp.351-362
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• 2020

Adaptive behaviors are one of ubiquitous features in evolutionary dynamics of populations, and certain adaptive behaviors can be witnessed by individuals' movements which are generally affected by local environments. In this paper, by revisiting the previous work, we investigate the sensitivity of species coexistence in the system of cyclic competition where species movement can be affected by local environments. By measuring the extinction probability through Monte-Carlo simulations, we find the relativistic effect of weights of local fitness and exchange rate for adaptive movement on species biodiversity which promotes species coexistence as the relativistic effect is intensified. In addition, by means of basins of initial conditions, we also found that adaptive movement can also affect species biodiversity with respect to the choice of initial conditions. The strong adaptive movement can eventually lead the coexistence as a globally stable state in the spatially extended system regardless of mobility.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.410-412
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• 2003

급속히 발전하는 컴퓨터 게임 산업에서, 플레이어는 주로 주어진 환경 내에서 캐릭터를 선정하여 플레이를 할 수 있다. 이러한 제한된 상황보다는 플레이어의 역할을 충실히 수행할 캐릭터를 선정하는 것은 좀 더 흥미로운 게임의 전개하는 방법일 것이다. 이 논문에서는 진화하는 디자인에 맞추어 게임 캐릭터 디자인의 다양한 형태를 위해 유전자 알고리즘을 적용하여 제안하였다. 플레이어를 대신할 가상 공간에서 좀 더 흥미로운 개체를 만들어 게임을 진행하는 것은 게임에 대한 흥미를 더욱 가중시킬 것이다.

• Journal of Korea Game Society
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• v.17 no.5
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• pp.123-132
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• 2017

The path-finding algorithm refers to an algorithm for navigating the route order from the current position to the destination in a virtual world in a game. The conventional path-finding algorithm performs graph search based on cost such as A-Star and Dijkstra. A-Star and Dijkstra require movable node and edge data in the world map, so it is difficult to apply online games with lots of map data. In this paper, we provide a Heuristic-based Genetic Algorithm Path-finding(HGAP) using Genetic Algorithm(GA). Genetic Algorithm is a path-finding algorithm applicable to game with variable environment and lots of map data. It seek solutions through mating, crossing, mutation and evolutionary operations without the map data. The proposed algorithm is based on Binary-Coded Genetic Algorithm and searches for a path by performing a heuristic operation that estimates a path to a destination to arrive at a destination more quickly.