Fuzzy-state Q-learning-based human behavior suggestion system in intelligent sweet home

Authors:
Sunha Bae;Sang Wan Lee;Yong Soo Kim;Zeungnam Bien
Affiliations:
LIG NEX1 Co. Ltd., Seoul, Republic of Korea;IBM-KAIST Bio-computing Research Center, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea;Department of Computer Engineering, Daejeon University, Daejeon, Republic of Korea;School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

Citing 3
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IEEE Transactions on Fuzzy Systems

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Abstract

Memory impaired people, e.g., dementia people, requires careful social support. Dementia people are getting increased with very high rate especially. It has been reported that regular daily life can alleviate the symptom of the memory loss. Accordingly, human behavior suggestion is highly expected to help memory impaired people live regularly. In this paper, we propose a human behavior suggestion system based on Fuzzy-state Q-Learning for memory impaired person, and show its possible application in Intelligent Sweet Home. Specifically, we claim that an averaged frequency feature is an important factor. In order to evaluate the validity of the proposed human behavior suggestion system, we conduct experiments with a real world data set, INT DB. The experimental results show that the proposed system with the averaged frequency feature outperforms the existing system.