An empathy learning problem for HSI: To be empathic, self-improving and ambient

Roberto Legaspi, Satoshi Kurihara, Ken Ichi Fukui, Koichi Moriyama, Masayuki Numao

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Empathy is a learnable skill that requires experiential learning and practice of empathic ability for it to improve and mature. In the context of human-system interaction (HSI) this can mean that a system should be permitted to have an initial knowledge of empathy provision that is inaccurate or incomplete, but with this knowledge evolving and progressing over time through learning from experience. This problem has yet to be defined and dealt in HSI. This paper is an attempt to state an empathy learning problem for an ambient intelligent system to self-improve its empathic responses based on user affective states.

本文言語English
ホスト出版物のタイトル2008 Conference on Human System Interaction, HSI 2008
ページ209-214
ページ数6
DOI
出版ステータスPublished - 2008
外部発表はい
イベント2008 Conference on Human System Interaction, HSI 2008 - Krakow, Poland
継続期間: 2008 5 252008 5 27

出版物シリーズ

名前2008 Conference on Human System Interaction, HSI 2008

Other

Other2008 Conference on Human System Interaction, HSI 2008
CountryPoland
CityKrakow
Period08/5/2508/5/27

ASJC Scopus subject areas

  • Human-Computer Interaction

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