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

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

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

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2008 Conference on Human System Interaction, HSI 2008
Pages209-214
Number of pages6
DOIs
Publication statusPublished - 2008 Sep 22
Externally publishedYes
Event2008 Conference on Human System Interaction, HSI 2008 - Krakow, Poland
Duration: 2008 May 252008 May 27

Publication series

Name2008 Conference on Human System Interaction, HSI 2008

Other

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

Keywords

  • Empathic computing
  • Machine learning
  • User modeling and user-adaptive interfaces

ASJC Scopus subject areas

  • Human-Computer Interaction

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    Legaspi, R., Kurihara, S., Fukui, K. I., Moriyama, K., & Numao, M. (2008). An empathy learning problem for HSI: To be empathic, self-improving and ambient. In 2008 Conference on Human System Interaction, HSI 2008 (pp. 209-214). [4581435] (2008 Conference on Human System Interaction, HSI 2008). https://doi.org/10.1109/HSI.2008.4581435