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An Affective Utility Model of User Motivation for Counselling Dialogue Systems

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Future and Emerging Trends in Language Technology. Machine Learning and Big Data (FETLT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10341))

Abstract

Counselling dialogue systems are designed to help users to change and monitor their behaviours in order to achieve beneficial goals, such as the acquisition of healthy habits. To be effective, it is important that these systems include a model that accounts for the effort that users are investing to achieve the goals. However, most of the systems available nowadays carry out a naïve calculation based on the attained results, rather than on the reasons behind the successes or failures and their consequences for future user behaviour. In contrast to this, in this paper we propose a model that characterizes user motivation considering various aspects of psychological theories on subjective expected utility and attribution. Moreover, we provide a specification that allows carrying out calculations that replicate the users’ decision-making process considering its emotional implications. The model is general-purpose and can be employed in standard architectures to make interpretations that adapt to each user, thus fostering more flexible and personalized interactions.

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Author information

Authors and Affiliations

  1. Department of Languages and Computer Systems, University of Granada, Granada, Spain

    Zoraida Callejas

  2. Computer Science Department, Carlos III University of Madrid, Madrid, Spain

    David Griol

Authors
  1. Zoraida Callejas
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  2. David Griol
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Corresponding author

Correspondence to Zoraida Callejas .

Editor information

Editors and Affiliations

  1. University of Seville , Sevilla, Spain

    José F Quesada

  2. University of Seville , Seville, Spain

    Francisco-Jesús Martín Mateos

  3. University of Seville , Seville, Spain

    Teresa López Soto

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Callejas, Z., Griol, D. (2017). An Affective Utility Model of User Motivation for Counselling Dialogue Systems. In: Quesada, J., Martín Mateos , FJ., López Soto, T. (eds) Future and Emerging Trends in Language Technology. Machine Learning and Big Data. FETLT 2016. Lecture Notes in Computer Science(), vol 10341. Springer, Cham. https://doi.org/10.1007/978-3-319-69365-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-69365-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69364-4

  • Online ISBN: 978-3-319-69365-1

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