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Awareness based filtering - toward the Cooperative Learning in Human Agent Interaction
Tomohiro Yamaguchi,Takuma Nishimura,Keiki Takadama 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
We propose a new method of the recommender agent that supports to make clear the preference of a user. First, an overview of our coarse to fine recommendation system and the experimental results are described. Next we will discuss the effectiveness of our system. We interpret the meaning of our fixed recommendation strategy of the agent for contributing to restrain to prevent the user from his awareness. Then we will propose the awareness based filtering method to assist a user to be aware the true preference of the user. The main idea is to visualize both the coarse to fine preference space and the history of the recommendation in it.
The Pacific Ocean Route Optimization by Pittsburgh-style Learning Classifier System
Saori Iseya,Keiji Sato,Kiyohiko Hattori,Keiki Takadama 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper proposes the optimization method which extends Pittsburg-style Learning Classifier System(LCS) for Pacific Ocean route. In detail, the following extensions are introduced:(1) the unrealistic route deletion, (2) the route integration, and (3) the route rest time minimization and the anchor order change. To investigate the effectiveness of the proposed methods, this paper applies them into LCS to optimize the Pacific Ocean liner route using the actual transportation data. The intensive simulations have revealed following indications: (1) the generated routes using the proposed methods can produce the feasible routes that are hard to be found by the conventional method; and (2) our proposed methods contribute to creating the effective route set which has the short rest time, a small number of vessels, and high profit.
Sleep Stage Estimation by Learning Classifier System Towards Care Support for aged persons
Kazuyuki Hirose,Hiroyasu Matsushima,Kiyohiko Hattori,Keiki Takadama 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper proposes the sleep stage estimation method that can provide more accurate estimation than the conventional method and is robust to bad condition of humans without connecting any devices to human"s body. Our proposed method can extract the specific wave pattern required to estimate the sleep stage from heart beat data. Through the intensive simulations by using the actual data of the human subjects, the following implications have been revealed: (1) the proposed method can provide more accurate sleep stage estimation than the conventional methods, and (2) he sleep stage estimation is robust to the bad physical conditions.