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A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study

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Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14847))

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Abstract

Existing agent-based chatbot frameworks need seamless mechanisms to include explainable dialogic engines within the contextual flow. To this end, this paper presents a set of novel modules within the EREBOTS agent-based framework for chatbot development, including dialog-based plug-and-play custom algorithms, agnostic back/front ends, and embedded interactive explainable engines that can manage human feedback at run time. The framework has been employed to implement an explainable agent-based interactive food recommender system. The latter has been tested with 44 participants, who followed a nutrition recommendation interaction series, generating explained recommendations and suggestions, which were, in general, well received. Additionally, the participants provided important insights to be included in future work.

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Notes

  1. 1.

    https://flutter.dev/.

  2. 2.

    https://docs.docker.com/compose/.

  3. 3.

    https://www.pryv.com/.

  4. 4.

    https://www.mongodb.com/.

  5. 5.

    https://www.igniterealtime.org/projects/openfire/.

  6. 6.

    https://github.com/javipalanca/spade.

  7. 7.

    https://www.enzuzo.com/.

  8. 8.

    https://www.mysql.com/.

  9. 9.

    https://prosody.im/.

  10. 10.

    https://telegram.org/.

  11. 11.

    https://cdn.openai.com/papers/gpt-4.pdf.

  12. 12.

    https://www.pryv.com/.

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Acknowledgments

This work has been supported by the Chist-Era grant CHIST-ERA-19-XAI-005, and by (i) the Swiss National Science Foundation (G.A. 20CH21_195530), and (ii) the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).

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Authors and Affiliations

  1. University of Applied Sciences and Arts Western Switzerland HES-SO, Sierre, Switzerland

    Berk Buzcu, Yvan Pannatier, Michael Ignaz Schumacher & Jean-Paul Calbimonte

  2. Computer Science, Özyeğin University, Istanbul, Turkey

    Reyhan Aydoğan

  3. Interactive Intelligence, Delft University of Technology, Delft, Netherlands

    Reyhan Aydoğan

  4. University of Applied Sciences and Arts Western Switzerland HES-SO, Sion, Switzerland

    Davide Calvaresi

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  1. Berk Buzcu
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  4. Michael Ignaz Schumacher
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Corresponding author

Correspondence to Berk Buzcu .

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Editors and Affiliations

  1. University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

    Andrea Omicini

  4. Ozyegin University, Istanbul, Türkiye

    Reyhan Aydogan

  5. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

    Rachele Carli

  6. Alma Mater Studiorum - Università di Bologna, Bologna, Italy

    Giovanni Ciatto

  7. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Joris Hulstijn

  8. Umeå University, Umeå, Sweden

    Kary Främling

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Buzcu, B., Pannatier, Y., Aydoğan, R., Ignaz Schumacher, M., Calbimonte, JP., Calvaresi, D. (2024). A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2024. Lecture Notes in Computer Science(), vol 14847. Springer, Cham. https://doi.org/10.1007/978-3-031-70074-3_4

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