• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.290-299
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• 2009

Evolutionary Artificial Neural Networks (EANNs) has been highly effective in Artificial Intelligence (AI) and in training NPCs in video games. When EANNs is applied to design game NPCs' smart AI which can make the game more interesting, there always comes two important problems: the more complex situation NPCs are in, the more complex structure of neural networks needed which leads to large operation cost. In this paper, the Dynamic State Evolutionary Neural Networks (DSENNs) is proposed based on EANNs which deletes or fixes the connection of the neurons to reduce the operation cost in evolution and evaluation process. Darwin Platform is chosen as our test bed to show its efficiency: Darwin offers the competitive team game playing behaviors by teams of virtual football game players.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1714-1725
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• 2007

This paper describes a top-down behavior planning framework in a simulation game from personality to real life action selection. The combined behavior creating system is formed by five levels of specification, which are personality definition, motivation extraction, emotion generation, decision making and action execution. Along with the data flowing process in our designed framework, NPC selects actions autonomously to adapt to the dynamic environment information resulting from active agents and human players. Furthermore, we illuminate applying Gaussian probabilistic distribution to realize character's behavior changeability like human performance. To elucidate the mechanism of the framework, we situated it in a restaurant simulation game.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.43-61
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• 2015

The valuation of patents is very important, since technology competitiveness is crucial for firms to maintain global competitiveness. But, the patent valuation is difficult and challenging because of the uncertainty affecting their returns. Hence, we propose a patent valuation method by incorporating game theory into the real option model, which can not only potentially recognizes the effect of uncertainty on patent value, but also consider investment decision in a competitive market, as a game between firms. With the model, we can consider dynamic strategy involving an option of patent leveraging strategies enabling the firm to switch among compete, cooperate, or wait modes under different demand or competitive advantage condition.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.567-574
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• 2013

In this paper, we have proposed an architectural solution for a system for the visualization and modification of large amounts of data. The pattern is based on an asynchronous execution of programmable commands and a reflective approach of an object structure composition. The described pattern provides great flexibility, which helps adopting it easily to custom application needs. We have implemented a system based on the described pattern. The implemented system presents an innovative approach for a dynamic data object initialization and a flexible system for asynchronous interaction with data sources. We believe that this system can help software developers increase the quality and the production speed of their software products.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.199-203
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• 2009

Recently, Massively Multiplayer Online Role-Playing Games (MMORPGs) has become increasingly popular, which continue to increase the number of game player. The volume of the game world also has been extended on a large scale. Existing Map-based distributed server architecture divides the game world into the rectangular regions and allocates the registered player in each region to the server responsible for that region. Players tend to concentrate in certain regions of the game world, which makes some special server overloaded. This paper proposes to change the boundary between servers to solve such a unbalanced load problem. Our proposed method first finds the overloaded server and then searches for its neighboring lightest loaded server to share with the overload evenly. Finally we implemented performance evaluation to demonstrate the efficiency of this approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• 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.

• Management Science and Financial Engineering
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• v.19 no.2
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• pp.31-36
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• 2013

We propose a war-game model that is appropriate for a raid-type warfare in which, a priori, the maneuver of the attacker is relatively certain. The model is based on a multi-weapon extention of the Lanchester's law. Instead of a continuous time dynamic game with the differential equations from the Lanchester's law, however, we adopt a multi-period model relying on a time-discretization of the Lanchester's law. Despite the obvious limitation that two players make a move only on the discrete time epochs, the pragmatic model has a manifold justification. The existence of an equilibrium is readily established by its equivalence to a finite zero-sum game, the existence of whose equilibrium is, in turn, well-known to be no other than the LP-duality. It implies then that the war-game model dictates optimal strategies for both players under the assumption that any strategy choice of each player will be responded by a best strategy of her opponent. The model, therefore, provides a sound ground for finding an efficient reinforcement of a defense system that guarantees peaceful equilibria.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.95-104
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• 2009

Recently, increasing numbers of games and simulators are being implemented by employing the physically-based modeling techniques for better realism. In this paper, we propose the implementation techniques for overhead crane simulator based on ODE, the well-known open source dynamic engine. By comparing the dynamic behavior of the proposed system with a commercial engine based simulator, the physical plausibility and the effectiveness of the ODE based OHC simulator are verified. We expect the proposed the OHC simulator can be successfully utilized for virtual training in various educational institutes.

• Journal of Korea Game Society
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• v.13 no.5
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• pp.19-30
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• 2013

This Study analyzes dynamic of a puzzle game play by applying entropy law. Entropy is a concept that a quantitative measure of the amount of thermal energy not available to do work in a closed system. And amount of entropy can be measured only if we see the closed system as whole, the field. Puzzle game is also closed system. When player moves an object in game, it change a relationship among objects in play field. In , through an act of position change, player sustains a play field active. In respect of an entropy, this kind of play is considered as pursue of usability of the energy. In , player piles up objects without empty space. In respect of an entropy, this kind of play is considered as pursue of the order. Likewise, puzzle game play can be considered as simulation of a human's pursue of the order in an entropy increasing physical world. And this pursue is a driving force of puzzle game play.