• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.400-402
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• 2000

This paper presents a game theoretic approach for power transactions analysis in a competitive market. The considered competitive power market is regarded as PooICo model, and the participating players are restricted by only two generating entities for simplicity in this paper. The analysis is performed on the basis of marginal cost based relations of bidding price and bidding generations. That is, we assume that the bidding price of each player is determined by the marginal cost when the bidding generation is pre-determined. This paper models the power transaction as a two player game and analyzes by applying the Nash eauilibrium idea. The generalized game model for power transactions covering constant-sum(especially zero-sum), and nonconstant-sum game is developed in this paper. Also, the analysis for each game model are performed in the case studies. Here, we have defined the payoff of each player as the weighted sum of both player's profits.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1344-1346
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• 1999

This paper presents a game theoretic approach for power transactions analysis in a competitive market. The considered competitive power market is regarded as PoolCO model, and the participating players are restricted by only two generating entities for simplicity in this paper. The analysis is performed on the basis of marginal cost based relations of bidding price and bidding generations. That is, we assume that the bidding price of each player is determined by the marginal cost when the bidding generation is pre-determined. This paper models the power transaction as a two player game and analyzes by applying the Nash eauilibrium idea. The generalized game model for power transactions covering constant-sum(especially zero-sum), and nonconstant-sum game is developed in this paper. Also, the analysis for each game model are Performed in the case studies. Here, we have defined the payoff of each player as the weighted sum of both player's profits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4157-4175
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• 2020

Moving target defense, as a 'game-changing' security technique for network warfare, realizes proactive defense by increasing network dynamics, uncertainty and redundancy. How to select the best countermeasure from the candidate countermeasures to maximize defense payoff becomes one of the core issues. In order to improve the dynamic analysis for existing decision-making, a novel approach of selecting the optimal countermeasure using game theory is proposed. Based on the signal game theory, a multi-stage adversary model for dynamic defense is established. Afterwards, the payoffs of candidate attack-defense strategies are quantified from the viewpoint of attack surface transfer. Then the perfect Bayesian equilibrium is calculated. The inference of attacker type is presented through signal reception and recognition. Finally the countermeasure for selecting optimal defense strategy is designed on the tradeoff between defense cost and benefit for dynamic network. A case study of attack-defense confrontation in small-scale LAN shows that the proposed approach is correct and efficient.

• 대한전기학회논문지:전력기술부문A
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• 제52권9호
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• pp.542-549
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• 2003

In this paper, a novel approach to generator maintenance scheduling strategy in competitive electricity markets based on non-cooperative dynamic game theory is presented. The main contribution of this study can be considered to develop a game-theoretic framework for analyzing strategic behaviors of generating companies (Gencos) from the standpoints of the generator maintenance-scheduling problem (GMP) game. To obtain the equilibrium solution for the GMP game, the GMP problem is formulated as a dynamic non-cooperative game with complete information. In the proposed game, the players correspond to the profit-maximizing individual Gencos, and the payoff of each player is defined as the profits from the energy market. The optimal maintenance schedule is defined by subgame perfect equilibrium of the game. Numerical results for two-Genco system by both proposed method and conventional one are used to demonstrate that 1) the proposed framework can be successfully applied in analyzing the strategic behaviors of each Genco in changed markets and 2) both methods show considerably different results in terms of market stability or system reliability. The result indicates that generator maintenance scheduling strategy is one of the crucial strategic decision-makings whereby Gencos can maximize their profits in a competitive market environment.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.207-216
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• 2000

Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human interactions suggests that if decision makers have prior information on opponents and furthermore learn the behaviors of other agents from interaction, the overall payoff would increase. We propose a new methodology for a strategy finding process using data mining in autonomous negotiation system ; ANSIA (Autonomous Negotiation System using Intelligent Agent). ANSIA is a strategy based negotiation system. The framework of ANSIA is composed of following component layers : 1) search agent layer, 2) data mining agent layer and 3) negotiation agent layer. In the data mining agent layer, that plays a key role as a system engine, extracts strategy from the historic negotiation is extracted by competitive learning in neural network. In negotiation agent layer, we propose the autonomous negotiation process model that enables to estimate the strategy of opponent and achieve interactive settlement of negotiation. ANISIA is motivated by providing a computational framework for negotiation and by defining a strategy finding model with an autonomous negotiation process.

• 한국정보처리학회논문지
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• 제5권6호
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• pp.1575-1582
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• 1998

본 논문에서는 멀티에이전트 환경상에서 에이전트 그룹들이 문제 풀이 과정이 변화할 때 적절한 협력대상을 선택하여 문제 해결 능력을 증가 시킬 수 있도록 협력 모델을 설계하고 평가 하였다. 그룹간 협력의 방향과 범위를 결정하기 위해 그룹 이익함수를 사용하였고 그룹을 협력 관련 부분과 비 관련 부분으로 분리 운영하였다. 이렇게 상황에 따라 재구성되는 그룹들을 그룹 수준에서 운영하기 위해 기존의 자료와 목표(goal)가 아닌 그룹의도(group intention)를 제어 기준으로 사용하였다. 이로 인해 그룹들은 먼저 그룹의도를 결정하고 그룹의도의 범위 안에서 현 상황에 적절한 전략을 선택하는 계층적인 제어가 가능하게 되었다. 이상의 제안들은 추적게임 테스트베드에 적용하여 실험/평가 되었다.

• 산업경영시스템학회지
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• 제37권1호
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• pp.120-126
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• 2014

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• 산업경영시스템학회지
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• 제42권4호
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• pp.39-48
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• 2019

This paper considers the Prisoner's Dilemma Game in which there exists a dilemma that the best response is that both players are to confess, but doing not confess can give a higher gain to the both players in a social perspective. To resolve such a dilemma in the game, an incentive model to encourage to confess and a penalty model for being imposed when not confessing are introduced, respectively. Then, the conditions are characterized under which incentive or penalty involved in the game's payoffs can make the game rational without a dilemma on both the personal and social perspectives, by taking the payoff values as variables with the incentive and penalty factors. Furthermore, it turns out that the resulting values of incentive and penalty are inversely proportional to each other, and thus, obtaining one of these amounts can provide the other. Simple examples are shown to interpret the theoretical verifications of our models, and randomly generated data based simulation results investigate the tendency of incentive and penalty and the resulting game values for a variety of instances. These results can provide a framework on resolving the dilemma by artificially putting incentive or penalty, although it is careful to apply more generalized real world games.

• 대한인간공학회지
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• 제34권2호
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• pp.103-113
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• 2015

Objective: This study proposes a systematic process to present the analysis methods and solutions of organizational root causes to human errors on the railroad. Background: In fact, organizational root cause such as organizational culture is an important factor in the safety concerns on human errors in the nuclear power plant, railroad and aircraft. Method: The proposed process is as follows: 1) define analysis boundary 2) select human error taxonomy 3) perform accident analysis 4) draw root causes with FGI 5) review root causes analysis with survey 6) chart analysis of root causes, and 7) propose alternatives and solutions. Results: As a result, root causes of the organizations like railroad and nuclear power plant came from the educational problems, violations, payoff system, safety culture and so forth. Conclusion: The proposed process does predict potential railroad accident through retrospect error analysis by building new human error taxonomies and problem solution. Application: This study would contribute to examination of the relationship between human error-based accidents and organizational root causes.