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빅데이터 시대의 개인정보 제공의도에 영향을 미치는 요인
민현홍(Hyeonhong Min),박성배(Seongbae Park),정진섭(Jinseop Jung),한경석(Kyeongseok Han) 한국인터넷전자상거래학회 2016 인터넷전자상거래연구 Vol.16 No.1
Recently, the appearance of “Big Data” is drawing great attention from managerial scholars and journals worldwide such as Harvard Business Review, New York Times, etc. It is also considered as the most attractive industry in the 21st century. The Big Data industry, however, is not the leakage form of large amount of information but the technique itself is based on information leakage. Through smart devices and social network services, information is automatically collected and is leaked. Therefore, the need of supervising “Big Data” begins to appear. Unlike the prior studies of privacy, this research focuses mainly on the privacy of a so-called social Big Data, which is transplanted form between Big Data and SNS. In the era of Big Data, anyone can encounter the risks and dangers related to the information leakage, and the disclosure of personal information. The purpose of this research, therefore, is to provide a model to analyze and authenticate the associated issues. Among the independent factors, the most influential variable to the perception of personal risk ranges from privacy policy, experience of personal information invasion, and privacy awareness. The most effective factor to the trust also ranges from privacy policy, experience of personal information invasion, and the privacy awareness. The analysis indicates that there are differences between groups varying in age.
임수연(SooYeon Lim),손기준(KiJun Son),박성배(SeongBae Park),이상조(SangJo Lee) 한국지능시스템학회 2005 한국지능시스템학회논문지 Vol.15 No.1
강화학습(Reinforcement-Learning)의 목적은 환경으로부터 주어지는 보상(reward)을 최대화하는 것이며, 강화학습 에이전트는 외부에 존재하는 환경과 시행착오를 통하여 상호작용하면서 학습한다. 대표적인 강화학습 알고리즘인 Q-Learning은 시간 변화에 따른 적합도의 차이를 학습에 이용하는 TD-Learning의 한 종류로서 상태공간의 모든 상태-행동 쌍에 대한 평가 값을 반복 경험하여 최적의 전략을 얻는 방법이다. 본 논문에서는 강화학습을 적용하기 위한 예를 n-Queen 문제로 정하고, 문제풀이 알고리즘으로 Q-Learning을 사용하였다. n-Queen 문제를 해결하는 기존의 방법들과 제안한 방법을 비교 실험한 결과, 강화학습을 이용한 방법이 목표에 도달하기 위한 상태전이의 수를 줄여줌으로써 최적 해에 수렴하는 속도가 더욱 빠름을 알 수 있었다. The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy through repeated experience of evaluation of all state-action pairs in the state space. This study chose n-Queen problem as an example, to which we apply reinforcement learning, and used Q-Learning as a problem solving algorithm. This study compared the proposed method using reinforcement learning with existing methods for solving n-Queen problem and found that the proposed method improves the convergence rate to the optimal solution by reducing the number of state transitions to reach the goal.