人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
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19 巻 , 3 号
選択された号の論文の3件中1~3を表示しています
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論文
  • Yongguang Bao, Xiaoyong Du, Mingrong Deng, Naohiro Ishii
    19 巻 (2004) 3 号 p. 166-173
    公開日: 2004/03/09
    ジャーナル フリー
    In the process of data mining of decision table using Rough Sets methodology, the main computational effort is associated with the determination of the reducts. Computing all reducts is a combinatorial NP-hard computational problem. Therefore the only way to achieve its faster execution is by providing an algorithm, with a better constant factor, which may solve this problem in reasonable time for real-life data sets. The purpose of this presentation is to propose two new efficient algorithms to compute reducts in information systems. The proposed algorithms are based on the proposition of reduct and the relation between the reduct and discernibility matrix. Experiments have been conducted on some real world domains in execution time. The results show it improves the execution time when compared with the other methods. In real application, we can combine the two proposed algorithms.
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  • 藤本 武司, 砂山 渡, 山口 智浩, 谷内田 正彦
    19 巻 (2004) 3 号 p. 174-183
    公開日: 2004/03/23
    ジャーナル フリー
    This paper describes a system to support transmission of human focusing skill by visualized their gaze behavior in Kansei interaction. In order to share personal Kansei information with other people, we need to transform it into cleared information. If this information is expressed by visualized style and transmitted to human from human, Kansei interaction is more creative than ever before. Our research group focuses on human gaze behavior that naturally reflects human actions, intentions and knowledge. Generally, it is difficult for us to feel other people's gaze behavior. Therefore, we constructed a VR-space, called ``Mirror Agent System'', where some users can work together and their gaze are visualized coincidentally. By using this system, a user can become aware of not only a gaze history of himself, but also other user's them, while looking at the scenes in the VR-space. When he can feel other's gaze behavior, he can guess their actions, intentions and knowledge. In this way, we expected to promote human-human Kansei interaction, and to improve user's focusing skill. However, our privious system lacked to visualize only useful parts from a gaze history, because following two problems. First problem is the quantity problem. If the quantity of a gaze history increases in the VR-space gradually, the background scenes are covered with the visualized history and a user feels interfereing in his work. Second problem is the quality problem. It is very difficult for a user to interpret other user's gaze history.
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  • 中嶋 宏, 森島 泰則, 山田 亮太, Scott Brave, Heidy Maldonado, Clifford Nass, 川路 茂保
    19 巻 (2004) 3 号 p. 184-196
    公開日: 2004/04/06
    ジャーナル フリー
    In this information society of today, it is often argued that it is necessary to create a new way of human-machine interaction. In this paper, an agent with social response capabilities has been developed to achieve this goal. There are two kinds of information that is exchanged by two entities: objective and functional information (e.g., facts, requests, states of matters, etc.) and subjective information (e.g., feelings, sense of relationship, etc.). Traditional interactive systems have been designed to handle the former kind of information. In contrast, in this study social agents handling the latter type of information are presented. The current study focuses on sociality of the agent from the view point of Media Equation theory. This article discusses the definition, importance, and benefits of social intelligence as agent technology and argues that social intelligence has a potential to enhance the user's perception of the system, which in turn can lead to improvements of the system's performance. In order to implement social intelligence in the agent, a mind model has been developed to render affective expressions and personality of the agent. The mind model has been implemented in a human-machine collaborative learning system. One differentiating feature of the collaborative learning system is that it has an agent that performs as a co-learner with which the user interacts during the learning session. The mind model controls the social behaviors of the agent, thus making it possible for the user to have more social interactions with the agent. The experiment with the system suggested that a greater degree of learning was achieved when the students worked with the co-learner agent and that the co-learner agent with the mind model that expressed emotions resulted in a more positive attitude toward the system.
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