Interaction reproducing model: A model for giving supports appropriate to user state

Hideki Aoyama, Motoyuki Ozeki, Yuichi Nakamura

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

1 Citation (Scopus)

Abstract

This paper presents a novel Interaction Reproducing Model (IRM) for the purpose of adjusting computer user support to match the state of the user, and it consists of a set of Interaction Finite State Machines (I-FSMs). Each I-FSM is trained using actual interaction records, and it represents an ideal interaction pattern for a user state. It can choose an appropriate system action by reproducing the interaction pattern of the I-FSM most similar to the current interaction. We developed a prototype teaching system, and conducted preliminary experiments. The results show that user impressions using our approach were better than when using the system without our approach.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings
Pages426-435
Number of pages10
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event9th Pacific Rim Conference on Multimedia, PCM 2008 - Tainan, Taiwan, Province of China
Duration: 2008 Dec 92008 Dec 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5353 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific Rim Conference on Multimedia, PCM 2008
CountryTaiwan, Province of China
CityTainan
Period08/12/908/12/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Aoyama, H., Ozeki, M., & Nakamura, Y. (2008). Interaction reproducing model: A model for giving supports appropriate to user state. In Advances in Multimedia Information Processing - PCM 2008 - 9th Pacific Rim Conference on Multimedia, Proceedings (pp. 426-435). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5353 LNCS). https://doi.org/10.1007/978-3-540-89796-5_44