• Journal of Communications and Networks
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• 제18권3호
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2454-2467
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• 2015

Increasing traffic and bandwidth requirements bring challenges to the next generation wireless networks (5G). As one of the main technology in 5G networks, Ultra-Dense Network (UDN) can be used to improve network coverage. In this paper, a radio over fiber based model is proposed to solve the load balancing problem in ultra-dense network. Stochastic differential game is introduced for the load balancing algorithm, and optimal load allocated to each access point (RAP) are formulated as Nash Equilibrium. It is proved that the optimal load can be achieved and the stochastic differential game based scheme is applicable and acceptable. Numerical results are given to prove the effectiveness of the optimal algorithm.

• 한국경영과학회지
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• 제3권2호
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• pp.41-45
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• 1978

Duel models are frequently used in war game simulation. Both game-theoretic approach and stochastic approach are applied to duel situations in war game. Game-theoretic models are usually classified into three categories, noisy duel, silent duel, and duel of continuous firing. Stochastic duels are classified depending upon assumptions. In this paper formulation and a general solution for each model will be summarized.

• Journal of Platform Technology
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• 제9권1호
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• pp.3-14
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• 2021

Cognitive Radio network (CR) is a promising paradigm that helps the unlicensed user (Secondary User) to analyse the spectrum and coordinate the spectrum access to support the creation of common control channel (CCC). The cooperation of secondary users and broadcasting between them is done through transmitting messages in CCC. In case, if the control channels may get jammed and it may directly degrade the network's performance and under such scenario jammers will devastate the control channels. Hopping sequences may be one of the predominant approaches and it may be used to fight against this problem to confront jammer. The jamming attack can be alleviated using one of the game modelling approach and in this proposed scheme stochastic games has been analysed with more single users to provide the flexible control channels against intrusive attacks by mentioning the states of each player, strategies ,actions and players reward. The proposed work uses a modern player action and better strategic view on game theoretic modelling is stochastic game theory has been taken in to consideration and applied to prevent the jamming attack in CR network. The selection of decision is based on Q learning approach to mitigate the jamming nodes using the optimal MDP decision process

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• 한국게임학회 논문지
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• 제2권2호
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• pp.52-59
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• 2002

이 논문에서는 FSM과 확률적 FSM, NFA 등이 게임에서 NPC의 행동 지정에 쓰인 방식을 소개하고, 기존 방법에서 확률적 FSM이나 NFA의 단점을 보완할 수 있는 새로운 확률적 FSM 방식을 제안한다. 즉, 확률 유한오토마타의 추론 방식을 이용하여 다양한 NPC나 컴퓨터 플레이어의 인성이나 특성을 자동적으로 게임에 반영하기 위한 방법을 제안한다. 이 방법으로 수 많은 게이머들의 인성이나 특성을 자동적으로 파악하여, 실제 게임에서 사용되는 NPC나 컴퓨터 플레이어에게 부여해 줄 수 있고, 또한 NPC들의 인성을 다양하게 부여함으로써 게임의 재미를 더 향상시킬 수가 있다.

• 한국게임학회 논문지
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• 제16권3호
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• pp.77-86
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• 2016

게임 산업 시장이 모바일 게임중심으로 변화하며, 모바일 게임 상의 가상전자화폐 구매는 게임업체의 주요 수익창출요소가 되어 왔다. 특히 확률게임 원리가 도입된 캐릭터 뽑기 시스템은 가상전자화폐를 이용한 대표적인 수익창출요소이다. 본 연구에서는 그러한 게임업체 가운데서도 이례적인 매출을 기록한 일본의 겅호 엔터테인먼트의 모바일 게임 퍼즐 앤 드래곤의 캐릭터 뽑기 시스템에 과금하는 사용자들의 심리를 분석하였다. 연구의 결과 도출된 세 가지 심리적 요인은 다음과 같다: (1) 갓 페스티벌의 확률게임에 적용되는 도박적 심리반응 추구와 인지적 오류의 발생 (2) 기간한정 갓 페스티벌과 콜라보의 희소성메시지의 전달을 통한 반응심리 (3) 친구시스템 및 캐릭터 콜렉션을 통한 개인적 만족감의 추구.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5820-5834
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• 2017

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제6권7호
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• pp.315-320
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• 2017

본 논문에서는 무선 네트워크 환경에서 악의적인 사용자의 인증서를 폐지하여 네트워크의 안정화를 지원하는 효율적인 기법을 제안한다. 제안된 기법은 무선장치 내의 침입탐지시스템을 기반으로 실시간으로 이웃노드의 악의적인 행동을 감지한다. 침입탐지시스템의 판단은 오차가 발생할 수 있으므로 오차를 보완하여 정확한 악의적인 노드의 인증서를 폐지하기위해 신뢰도기반의 가중투표게임과 확률러닝을 사용하여 정확성을 높일 수 있었다. 폐지과정을 통해 제안된 기법이 동적인 이동 애드혹 네트워크 환경에 효율적으로 적용되는 것을 알 수 있었으며 컴퓨터 시뮬레이션을 통해 기존에 제안된 다른 기법에 비해 악의적인 she의 인증서 폐지 성공률과 네트워크의 안정성 부분에서 좋은 성능을 보였다.

• 스마트미디어저널
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• 제9권4호
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• pp.36-43
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• 2020

The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.