• Journal of Korea Game Society
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• v.5 no.4
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• pp.39-48
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• 2005

The gift coupon system was introduced in the AWP(Amusement With Prize) Game Industry in February 2002 to revitalize the sluggish domestic game industry and establish the cultural industry base. Today, however, speculations regarding these coupons are fast becoming a serious social issue. Therefore, this study proposes the establishment of the MCTF Management System as a possible solution to the analysis and resolution of the problem of gift coupons aimed at promoting the AWP Game Industry and ensuring its soundness as well as bridging the gap between the domestic and international game markets in terms of the industrial structure; thus securing global competitiveness. Unlike any other measure taken to date to prevent speculations, the MCTF Management System can eradicate problems related to coupons offered for free in the AWP Game Industry. This strategic system consists of Merchandise Coupon Management, Central Management, Tripartite Guarantee Management, and Fund Management System. Finally, this system is expected to help root out the problems related to gift coupons.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.103-110
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• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.1-6
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• 2021

Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study proposes a method in which we can easily design reinforcement learning agents and implement game play agents by applying PPO(Proximal Policy Optimization) algorithms. And we could identify the performance was increased about 44% than the conventional method. The tools we used are the Unity 3D game engine and Unity ML SDK. The experimental result shows that agents became to learn game rules and make better strategic decisions as experiments go on. On average, the puzzle gameplay agents implemented in this study played puzzle games better than normal people. It is expected that the designed agent could be used to speed up the game level design process.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.83-90
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• 2004

Strategic games are missing special qualities of genre these days. Game engines neither reason about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components -“user behavior monitor”to abstract user's objects behavior,“learning engine”to learn user's strategy,“behavior display handler”to reflect abstracted behavior of computer objects on game. Especially, this paper proposes two layered-structure to apply non-monotonic reasoning and inductive learning to make behaviors of computer objects that learns strategy behaviors of user objects exactly, and corresponds in user's objects. The engine decides actions and strategies of computer objects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monotonic reasoning and inductive machine learning.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.159-179
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• 2024

Based on the case of collaboration between large companies and startups, this study suggests the importance of establishing mutual cooperation and trust relationships for the success of open innovation strategy from the perspective of cooperative game theory. It also provides implications for how this can be implemented. Due to information asymmetry and differences in organizational culture and decision-making structures between large companies and startups, collaboration is likely to proceed in the form of non-cooperative games among players in general open innovation, leading to the paradox of open innovation, which lowers the degree of innovation. Accordingly, this study conducted a case study on collaboration between large company 'G' and startup 'S' based on the research question "How did we successfully promote open innovation through cooperative game-type collaboration?" The study found that successful open innovation requires (1) setting clear collaboration goals to solve the organizational problem between large companies and startups, (2) supporting human resources for qualitative growth of startups to solve reliability problems, (3) leading to strategic investment and joint promotion of new projects to solve the profit distribution problem. This study is significant in that it contributes to expanding the discussion of the success factors of open innovation to the importance of interaction and strategic judgment considering the organizational culture and decision-making structure among players, and empirically confirming the success conditions of open innovation from the perspective of cooperative game theory.

• East Asian Economic Review
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• v.20 no.2
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• pp.191-228
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• 2016

This paper analyzes the role of absorptive capacity in R&D spillovers through strategic R&D investments in a game-theoretic framework. In the model, a firm's effective R&D is composed of idiosyncratic R&D, which produces its own innovations, and identical R&D, which improves absorptive capacity. The model shows that in the presence of absorptive capacity firms have a tendency to underinvest (overinvest) in idiosyncratic (identical) R&D relative to the social optimum. As the spillover becomes larger, firms decrease their own R&D while they become more inclined towards strategic exploitation of rivals' efforts. Since the former effect overpowers the latter, the total amount of R&D decreases as the spillover increases. This is socially undesirable, providing a potential justification for a governmental subsidy for idiosyncratic R&D and a tax on identical R&D. The findings may have important implications for newly industrialized or emerging countries that consider a redirection of national R&D policy and intellectual property rights (IPR) regime.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.330-330
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• 2000

In this paper, as an entertainment robot the implementation of boxing robot system is presented for mechatronics education. In order for students to learn robot as a mechatronics system, boxing robot is a good model. The boxing robot consists of three parts: two link arms for punching, controller for actuating wheeled mobile robot, infrared rays sensors for the detection of he other robot and ring. The strategic algorithm for playing boxing is presented as wel as simple game rules.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.5
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• pp.214-219
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• 2006

Oligopoly differs from perfect competition and monopoly in that a firm must consider rival firms' behavior to determine its own best policy. This interrelationship among firms is the issue examined in this paper. In the oligopoly market, the complete information market means that each producer has full information about itself, the market, and its rivals. That is, each producer knows the market demand function, its own cost function and the cost functions of rivals. On the other hand, the incomplete information market means that in general each producer lacks full information about the market or its rivals. Here, we assume that each firm doesn't know the cost functions and the strategic biddings of its rivals. The main purpose of this paper is to analyze firm' strategic behaviors and equilibrium in an electricity market with incomplete information. In the case study, the complete information market and the incomplete market are compared at the Nash Equilibrium from the viewpoints of market price, transaction quantities, consumer benefits, and Social Welfare.

• Journal of agriculture & life science
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• v.46 no.6
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• pp.185-195
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• 2012

The purpose of this paper is to analyze how strategically the bilaterally monopolistic firms, only-one-seller and only-one-buyer, behave in a situation in which each firm has uncertain information on its opponent firm's cost. Even though the two firms know that seeking integrated profit leads to the optimized profit for both firms, each firm has an incentive to opportunistically behave to increase its share of the integrated profit. These opportunistic behaviors of the firms are analyzed through a game theoretic approach especially finding Nash equilibrium mixed strategies for the strategic profiles such as true-report or not and monitoring or not. The comparative statics to the Nash equilibrium mixed strategies shows that as the profit share increases the probability of monitoring an opponent firm is decreased while the probability increases as the size of the overstated production cost increases. This study also shows that high penalty and low monitoring cost lead to high probability to tell the truth of the production cost.

• Proceedings of the Korea Port Economic Association Conference
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• 2006.08a
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• pp.1-12
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• 2006

As national economies globalize, demand for intercontinental container shipping services is growing rapidly, providing a potential economic boon for the countries and communities that provide port services. On the promise of profits, many governments are investing heavily in port infrastructure, leading to a possible glut in port capacity, driving down prices for port services and eliminating profits as ports compete for business. Further, existing ports are making strategic investments to protect their market share, increasing the chance new ports will be overcapitalized and unprofitable. Governments and port researchers need a tool for understanding how local competition in their region will affect demand for port services at their location, and thus better assess the profitability of a prospective port. We propose to develop such a tool by extending our existing simulation model of global container traffic to incorporate demand-side shipper preferences and supply-side strategic responses by incumbent ports to changes in the global port network, including building new ports, scaling up existing ports, and unexpected port closures. We will estimate shipper preferences over routes, port attributes and port services based on US and international shipping data, and redesign the simulation model to maximize the shipper's revealed preference functions rather than simply minimize costs. As demand shifts, competing ports will adjust their pricing (short term) and infrastructure (long term) to remain competitive or defend market share, a reaction we will capture with a game theoretic model of local monopoly that will predict changes in port characteristics. The model's hypotheses will be tested in a controlled laboratory experiment tailored to local port competition in Asia, which will also serve to demonstrate the subtle game theoretic concepts of imperfect competition to a policy and industry audience. We will apply the simulation model to analyze changes in global container traffic in three scenarios: addition of a new large port in the US, extended closure of an existing large port in the US, and cooperative and competitive port infrastructure development among Korean partner countries in Asia.