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M-Path: A Conversational System for the Empathic Virtual Agent

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Book cover Biologically Inspired Cognitive Architectures 2019 (BICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 948))

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Abstract

M-Path is an embodied conversational agent developed to achieve natural interaction using empathic behaviors. This paper is aimed to describe the details of the conversational management system within the M-Path framework that manages dialogue interaction with an emotional awareness. Our conversational system is equipped with a goal-directed narrative structure that adapts to the emotional reactions of the user using empathy mechanisms. We further show the implementation and a preliminary evaluation of our system in a consultation scenario, where our agent uses text-based dialogue interaction to conduct surveys.

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References

  1. Bartneck C, Kulić D, Croft E, Zoghbi S (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot 1(1):71–81

    Article  Google Scholar 

  2. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Soft 67(1):1–48

    Article  Google Scholar 

  3. DeVault D, Artstein R, Benn G, Dey T, Fast E, Gainer A, Georgila K, Gratch J, Hartholt A, Lhommet M et al (2014) Simsensei kiosk: a virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 international conference on autonomous agents and multi-agent systems, pp 1061–1068

    Google Scholar 

  4. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding

    Google Scholar 

  5. Heerink M, Krose B, Evers V, Wielinga B (2009) Measuring acceptance of an assistive social robot: a suggested toolkit. In: RO-MAN 2009-The 18th IEEE international symposium on robot and human interactive communication, IEEE, pp 528–533

    Google Scholar 

  6. Kroenke K, Spitzer RL (2002) The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann 32(9):509–515

    Article  Google Scholar 

  7. Lisetti C, Amini R, Yasavur U, Rishe N (2013) I can help you change! an empathic virtual agent delivers behavior change health interventions. ACM Trans Manag Inf Syst (TMIS) 4(4):19

    Google Scholar 

  8. Loper E, Bird S (2002) NLTK: The natural language toolkit. In: Proceedings of the ACL workshop on effective tools and methodologies for teaching natural language processing and computational linguistics. Association for Computational Linguistics, Philadelphia

    Google Scholar 

  9. Paiva A, Leite I, Boukricha H, Wachsmuth I (2017) Empathy in virtual agents and robots: a survey. ACM Trans Interact Intell Syst (TiiS) 7(3):11

    Google Scholar 

  10. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  11. Provoost S, Lau HM, Ruwaard J, Riper H (2017) Embodied conversational agents in clinical psychology: a scoping review. J Med Internet Res 19(5):e151

    Article  Google Scholar 

  12. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  13. Nathan Spreng R, McKinnon MC, Mar RA, Levine B (2009) The toronto empathy questionnaire: scale development and initial validation of a factor-analytic solution to multiple empathy measures. J Pers Assess 91(1):62–71

    Article  Google Scholar 

  14. Yalçın ÖN (in press) Empathy framework for embodied conversational agents. Cogn Syst Res J

    Google Scholar 

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Acknowledgements

This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2019-06767].

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Authors and Affiliations

  1. School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC, Canada

    Özge Nilay Yalçın & Steve DiPaola

Authors
  1. Özge Nilay Yalçın
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  2. Steve DiPaola
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Corresponding authors

Correspondence to Özge Nilay Yalçın or Steve DiPaola .

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Editors and Affiliations

  1. Moscow Engineering Physics Institute (MEPhI), Department of Cybernetics, National Research Nuclear University (NRNU), Moscow, Russia

    Alexei V. Samsonovich

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Yalçın, Ö.N., DiPaola, S. (2020). M-Path: A Conversational System for the Empathic Virtual Agent. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_78

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