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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIEEE Virtual Reality Conference 2012, VR 2012 - Proceedings
DOIs
Publication statusPublished - 2012
Event19th IEEE Virtual Reality Conference, VR 2012 - Costa Mesa, CA, United States
Duration: 2012 Mar 42012 Mar 8

Publication series

NameProceedings - IEEE Virtual Reality

Other

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

Keywords

  • ambient information system
  • interaction sequence
  • profit-sharing
  • reinforcement learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Nakase, J., Moriyama, K., Kiyokawa, K., Numao, M., Oyama, M., & Kurihara, S. (2012). Adaptive interactive device control by using reinforcement learning in ambient information environment. In IEEE Virtual Reality Conference 2012, VR 2012 - Proceedings [6180848] (Proceedings - IEEE Virtual Reality). https://doi.org/10.1109/VR.2012.6180848