• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.253-261
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• 2004

Game theory is mathematical analysis developed to study involved in making decisions. In 1928, Von Neumann proved that every two-person, zero-sum game with finitely many pure strategies for each player is deterministic. As well, in the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) introduced by Maynard Smith. Keeping pace with these game theoretical studies, the first computer simulation of co-evolution was tried out by Hillis in 1991. Moreover, Kauffman proposed NK model to analyze co-evolutionary dynamics between different species. He showed how co-evolutionary phenomenon reaches static states and that these states are Nash equilibrium or ESS introduced in game theory. Since the studies about co-evolutionary phenomenon were started, however many other researchers have developed co-evolutionary algorithms, in this paper we propose Game theory based Co-Evolutionary Algorithm (GCEA) and confirm that this algorithm can be a solution of evolutionary problems by searching the ESS.To evaluate newly designed GCEA approach, we solve several test Multi-objective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by co-evolutionary algorithm and analyze optimization performance of GCEA by comparing experimental results using GCEA with the results using other evolutionary optimization algorithms.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.313-316
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• 2005

공진화 알고리즘은 두 개 이상의 개체군이 상호작용하며 진화하는 알고리즘이다. 기존의 진화 알고리즘이 하나의 개체군으로 구성된 정적인 적합도 지형에서 해를 찾는 방식임에 반해 공진화 알고리즘은 두개 이상의 개체군이 동적인 적합도 지형을 제공하여 더 강건하고 빠른 수렴성을 보인다. 본 논문에서는 GA가 풀기 어려운 GA-hard problem을 풀기 위하여 저자가 제안한 3가지 공진화 모델을 설명한다. 첫번째 모델은 찾고 자하는 해와 환경을 각각 경쟁하는 개체군으로 구성해 진화하는 방법으로 사용자의 환경설정에 의해 지역적 해를 찾는 것을 방지하는 경쟁적 공진화 알고리즘이다. 두 번째 모델은 찾고자하는 해와 이를 보조하는 스키마를 각각 개체군으로 구성해 진화하는 스키마 공진화 알고리즘이다. 세 번째 알고리즘은 해를 구성하는 부분을 두 개의 개체군으로 나누고 두 개체군이 서로 게임을 통해 진화하도록 하는 게임이론에 기반한 공진화 알고리즘이다.

• Cartoon and Animation Studies
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• s.10
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• pp.159-178
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• 2006

Digital Media have been advanced and disseminated at the same time around the world. Digital contents have been evolved while Product Technology and Process Technology of 3D Digital Animation and On-line game have completed the innovative developments. First of all, several problems can be occurred when product technology can not lead the evolvement of digital contents. To accomplish the evolvement of both product technology and process technology of digital contents, the whole production system and process system must be modulized. Modulization of system can be completed by the consistent and stable integration of platform. Modulization of production system can bring out the modulization of product technology and then the modulization of technology can speed up the commercialization and market test.

• Journal of Korea Game Society
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• v.11 no.2
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• pp.57-68
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• 2011

This study approaches how newly emerged game companies has implemented strategies for product diversification according to market growth cycle(beginninggrowing-mature) by empirical case study through evolutionary theory and resource based theory approach. At the beginning, online game companies had grown with different strategies(technology based, service based) by initial condition(genre, technological level, user attribute). After market growth, for product diversification, these companies carried out path-dependent alliance strategy(complementary, competitive) depending on resource base(technology capacity, service capacity based). As online game market getting mature, these companies has adapted flexibly in responding to market growth cycle by integrated strategy(naturally selected to mobilize every possible resource capability). By analyzing the alliance strategies pattern of online game companies in newly emerged game industry according to market growth cycle through combination of resource based theory and evolutionary theory, these results suggest that new industrial, theoretical, policy model is required.

• Journal of Korea Game Society
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• v.2 no.2
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• pp.60-65
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• 2002

In the majority of today's computer games, the behaviour of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on teaming ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.

• Journal of Korea Game Society
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• v.14 no.2
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• pp.29-44
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• 2014

This Study analyzes gameness in SNS. SNS is not only communication tool for real life but also game like media for the experimental fiction. As the media like games, SNS meets 6 classic game features. After the Tatradic analysis, Expressive and manipulation rules move SNS paidia direction, player's effort and negotiable outcome are obsolesced. Player's attachment is enhanced and in SNS, the tradition of MUD retrieves abstract ground with representative expression. As following result, SNS is able to extend its own area to the player's creative world for the possible. SNS is the media like games.

• Cartoon and Animation Studies
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• s.14
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• pp.151-164
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• 2008

Cross platform game has brought about the expansion of game market, which results in technology innovation overcoming the limit of game consumption. The new model integrates both off and online game services. Gamers can now enjoy game service regardless of age, time, and space. If the technology evolution model of digital contents like cross-platform game engine can provide contents for several platform at the same time, the interactive service can be utilized into maximum level. It is also necessary to allocate, switch data as well as to innovate the transmission technology of data according to each platform. Providing the same contents for several platform as many as possible can be the most suitable strategy to enhance the efficiency and profits. However if the interactive service can be accomplished completely, the development of data switching technology and distribution should be made. To be a leader in the next digital contents market, one should develop the network engine technology which can embody the optimization of consumption in the interactive network service.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.869-874
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• 2007

In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1309-1316
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• 2001

In the majority of todays animation and computer games, the behaviours of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on learning ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.