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Predicting the Priority of Social Situations for Personal Assistant Agents

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PRIMA 2020: Principles and Practice of Multi-Agent Systems (PRIMA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12568))

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Abstract

Personal assistant agents have been developed to help people in their daily lives with tasks such as agenda management. In order to provide better support, they should not only model the user’s internal aspects, but also their social situation. Current research on social context tackles this by modelling the social aspects of a situation from an objective perspective. In our approach, we model these social aspects of the situation from the user’s subjective perspective. We do so by using concepts from social science, and in turn apply machine learning techniques to predict the priority that the user would assign to these situations. Furthermore, we show that using these techniques allows agents to determine which features influenced these predictions. Results based on a crowd-sourcing user study suggest that our proposed model would enable personal assistant agents to differentiate between situations with high and low priority. We believe this to be a first step towards agents that better understand the user’s social situation, and adapt their support accordingly.

This work is part of the research programme CoreSAEP, with project number 639.022.416, which is financed by the Netherlands Organisation for Scientific Research (NWO).

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Notes

  1. 1.

    Icons used in Fig. 1 were made by Freepik and retrieved from www.flaticon.com.

  2. 2.

    https://www.mturk.com/.

  3. 3.

    The survey questions and the data can be found in the supplementary materials in https://doi.org/10.4121/13176923.

  4. 4.

    The code can be accessed under: https://github.com/ilir-kola/priority-social-situations.git.

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Author information

Authors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Ilir Kola, Myrthe L. Tielman & Catholijn M. Jonker

  2. Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

    Catholijn M. Jonker

  3. University of Twente, Enschede, The Netherlands

    M. Birna van Riemsdijk

Authors
  1. Ilir Kola
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  2. Myrthe L. Tielman
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  3. Catholijn M. Jonker
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  4. M. Birna van Riemsdijk
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Corresponding author

Correspondence to Ilir Kola .

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Editors and Affiliations

  1. Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  2. University of Tasmania, Tasmania, TAS, Australia

    Quan Bai

  3. University of Alcalá, Alcala de Henares, Spain

    Iván Marsá Maestre

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Kola, I., Tielman, M.L., Jonker, C.M., van Riemsdijk, M.B. (2021). Predicting the Priority of Social Situations for Personal Assistant Agents. In: Uchiya, T., Bai, Q., Marsá Maestre, I. (eds) PRIMA 2020: Principles and Practice of Multi-Agent Systems. PRIMA 2020. Lecture Notes in Computer Science(), vol 12568. Springer, Cham. https://doi.org/10.1007/978-3-030-69322-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-69322-0_15

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