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Towards Realtime Adaptation: Uncovering User Models from Experimental Data

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Book cover Knowledge Management and Acquisition for Intelligent Systems (PKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11016))

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Abstract

Virtual Worlds and the non-player characters that inhabit them often lack knowledge about their users. Users are treated as sources of input or feedback. At best, systems respond to the user’s behavioural data captured in logfiles. But there is no deep understanding of the player. Without this deep knowledge it is not possible for the computer to intelligently adapt. Relevant knowledge about the user will differ according to the application domain. Currently studies capture data such as biographical details, health status and history, psychological profiles, preferences and attitudes via questionnaires. This data can not be used in real time to influence the behaviour of the system. We suggest that data collected in past studies could be used to create user profiles and rules that can be used in real time for tailored interactions. We present two examples in this paper, one relating to an educational virtual world for science inquiry and the other involving the use of an Intelligent Virtual Agent to reduce study stress.

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Acknowledgements

This work was partially supported by the Australian Research Council Discovery Grant DP150102144 “Agent-based virtual learning environments for understanding science”. Thanks to participants in all of the studies.

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Authors and Affiliations

  1. Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, Australia

    Deborah Richards, Hedieh Ranjbartabar & Anupam Makhija

  2. Department of Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia

    Ayse Aysin Bilgin

Authors
  1. Deborah Richards
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  2. Ayse Aysin Bilgin
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  3. Hedieh Ranjbartabar
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  4. Anupam Makhija
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Corresponding author

Correspondence to Deborah Richards .

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Editors and Affiliations

  1. University of Tsukuba , Tokyo, Japan

    Kenichi Yoshida

  2. Shih Chien University, Taipei City, Taiwan

    Maria Lee

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Richards, D., Bilgin, A.A., Ranjbartabar, H., Makhija, A. (2018). Towards Realtime Adaptation: Uncovering User Models from Experimental Data. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-97289-3_4

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  • Online ISBN: 978-3-319-97289-3

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