Adaptive interactive device control by using reinforcement learning in ambient information environment

Junya Nakase, Koichi Moriyama, Kiyoshi Kiyokawa, Masayuki Numao, Mayumi Oyama, Satoshi Kurihara

研究成果: Conference contribution

抄録

In ambient information systems, not only extracting human behavior by sensor network but also adaptive autonomous interaction between the environment and humans is an important function. In this paper we propose a reinforcement learning framework to extract suitable interaction for each person from daily behavior. In the experiment, we show the feasibility of the proposed methodology.

本文言語English
ホスト出版物のタイトルIEEE Virtual Reality Conference 2012, VR 2012 - Proceedings
DOI
出版ステータスPublished - 2012
イベント19th IEEE Virtual Reality Conference, VR 2012 - Costa Mesa, CA, United States
継続期間: 2012 3 42012 3 8

出版物シリーズ

名前Proceedings - IEEE Virtual Reality

Other

Other19th IEEE Virtual Reality Conference, VR 2012
CountryUnited States
CityCosta Mesa, CA
Period12/3/412/3/8

ASJC Scopus subject areas

  • Engineering(all)

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