• ETRI Journal
• /
• v.31 no.1
• /
• pp.89-91
• /
• 2009

This letter considers the problem of resource sharing among a relay and multiple user nodes in cooperative transmission networks. We formulate this problem as a sellers' market competition and use a noncooperative game to jointly consider the benefits of the relay and the users. We also develop a distributed algorithm to search the Nash equilibrium, the solution of the game. The convergence of the proposed algorithm is analyzed. Simulation results demonstrate that the proposed game can stimulate cooperative diversity among the selfish user nodes and coordinate resource allocation among the user nodes effectively.

• Proceedings of the KIEE Conference
• /
• 2001.11b
• /
• pp.63-66
• /
• 2001

In the Poolco model, the bidding game is executed periodically. The player participating to the bidding game accumulates the information of others' strategies and payoffs through the repeated process. Thereby, he is able to map out how he gets his maximum profit, and proceed to the optimal strategy region. This paper shows the algorithm for a player to determine his strategy in t period based the information of the game results of t-1, t-2 period. And this algorithm can be formulated by using Dynamic game theory.

• Journal for History of Mathematics
• /
• v.22 no.1
• /
• pp.53-74
• /
• 2009

Black-out Game(Lightout, Merlin Game, ${\sigma}$+Game) is an interesting game on the chessboard, when you click a button with black or white color, it changes color of itself and other buttons who shares edges. With this rule, we win the game when we have a chessboard with all same color after we click some of the buttons of it. Pretty much of research has been made on founding the winnable strategy for this type of game. In this paper, we first introduce a history of mathematical modeling on this game. Then we develop an algorithm to offer a winnable blackout game of any size. Our tools also show our new algorithm works. Finally, we show how we can use this game in mathematics education.

• Journal of Korea Game Society
• /
• v.11 no.6
• /
• pp.61-70
• /
• 2011

In this paper, we propose a defense NPC control model in the soccer game by applying the Decision Tree learning algorithm. The proposed model extracts the direction patterns and the action patterns generated by many soccer game users, and applies these patterns to the Decision Tree learning algorithm. Then, the proposed model decides the direction and the action according to the learned Decision Tree. Experimental results show that the proposed model takes some time to learn the Decision Tree while the proposed model takes 0.001-0.003 milliseconds to decide the direction and the action based on the learned Decision Tree. Therefore, the proposed model can control NPC in the soccer game system in real time. Also, the proposed model achieves higher accuracy than a previous model (Letia98); because the proposed model can utilize current state information, its analyzed information, and previous state information.

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

• The KIPS Transactions:PartB
• /
• v.15B no.3
• /
• pp.253-258
• /
• 2008

Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.4 s.42
• /
• pp.17-25
• /
• 2006

An intelligent game has been studied for some time, and the main purpose of the study was to win against human by enhancing game skills. But some commercial games rather focused on adaptation of the user's behavior in order to bring interests on the games. In this study, we are suggesting an adaptive reinforcement learning algorithm, which focuses on the adaptation of user behavior. We have designed and developed the Othello game, which provides large state spaces. The evaluation of the experiment was done by playing two reinforcement learning algorithms against Min-Max algorithm individually. And the results show that our approach is playing more improved learning rate, than the previous reinforcement learning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.6
• /
• pp.1379-1397
• /
• 2013

We consider a system where a licensed radio spectrum is shared by multiple primary users(PUs) and secondary users(SUs). As the spectrum of interest is licensed to primary network, power and channel allocation must be carried out within the cognitive radio network so that no excessive interference is caused to PUs. For this system, we study the joint beamforming and power allocation problem via game theory in this paper. The problem is formulated as a non-cooperative beamforming and power allocation game, subject to the interference constraints of PUs as well as the peak transmission power constraints of SUs. We design a joint beamforming and power allocation algorithm for maximizing the total throughput of SUs, which is implemented by alternating iteration of minimum mean square error based decision feedback beamforming and a best response based iterative power allocation algorithm. Simulation results show that the algorithm has better performance than an existing algorithm and can converge to a locally optimal sum utility.

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.615-629
• /
• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• Journal of Korea Game Society
• /
• v.18 no.4
• /
• pp.83-98
• /
• 2018

The growth of a game character is represented by the addition and subtraction of the status value. The status factors are variously defined and used depending on the kind of games. In this paper, we propose how character 's status elements are defined and used. We also propose a method for assigning a status value when generating a character considering growth. This method is designed using genetic algorithms to link the growth of a character with an element of time and to generate a character with the appropriate status value according to the change of time. The proposed method is verified through experiments based on time variation.