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

Berk Buzcu; Yvan Pannatier; Reyhan Aydoğan; Michael Ignaz Schumacher; Jean Paul Calbimonte; Davide Calvaresi

doi:10.1007/978-3-031-70074-3_4

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

Berk Buzcu^*, Yvan Pannatier, Reyhan Aydoğan, Michael Ignaz Schumacher, Jean Paul Calbimonte, Davide Calvaresi

^*Corresponding author for this work

Interactive Intelligence

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers
Subtitle of host publication	Conference Proceedings
Editors	Davide Calvaresi, Amro Najjar, Andrea Omicini, Rachele Carli, Giovanni Ciatto, Reyhan Aydogan, Joris Hulstijn, Kary Främling
Place of Publication	Cham
Publisher	Springer
Pages	58-78
Number of pages	21
ISBN (Electronic)	978-3-031-70074-3
ISBN (Print)	979-8-3503-6204-6
DOIs	https://doi.org/10.1007/978-3-031-70074-3_4
Publication status	Published - 2024
Event	6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024 - Auckland, New Zealand Duration: 6 May 2024 → 10 May 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14847 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024
Country/Territory	New Zealand
City	Auckland
Period	6/05/24 → 10/05/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Chatbot Framework
Explainable AI
User Study

Access to Document

10.1007/978-3-031-70074-3_4

Cite this

Buzcu, B., Pannatier, Y., Aydoğan, R., Ignaz Schumacher, M., Calbimonte, J. P., & Calvaresi, D. (2024). A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. In D. Calvaresi, A. Najjar, A. Omicini, R. Carli, G. Ciatto, R. Aydogan, J. Hulstijn, & K. Främling (Eds.), Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers: Conference Proceedings (pp. 58-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14847 LNAI). Springer. https://doi.org/10.1007/978-3-031-70074-3_4

Buzcu, Berk ; Pannatier, Yvan ; Aydoğan, Reyhan et al. / A Framework for Explainable Multi-purpose Virtual Assistants : A Nutrition-Focused Case Study. Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers: Conference Proceedings. editor / Davide Calvaresi ; Amro Najjar ; Andrea Omicini ; Rachele Carli ; Giovanni Ciatto ; Reyhan Aydogan ; Joris Hulstijn ; Kary Främling. Cham : Springer, 2024. pp. 58-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study",

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.",

keywords = "Chatbot Framework, Explainable AI, User Study",

author = "Berk Buzcu and Yvan Pannatier and Reyhan Aydoğan and {Ignaz Schumacher}, Michael and Calbimonte, {Jean Paul} and Davide Calvaresi",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024 ; Conference date: 06-05-2024 Through 10-05-2024",

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doi = "10.1007/978-3-031-70074-3_4",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

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editor = "Davide Calvaresi and Amro Najjar and Andrea Omicini and Rachele Carli and Giovanni Ciatto and Reyhan Aydogan and Joris Hulstijn and Kary Fr{\"a}mling",

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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 D Calvaresi, A Najjar, A Omicini, R Carli, G Ciatto, R Aydogan, J Hulstijn & K Främling (eds), Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers: Conference Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14847 LNAI, Springer, Cham, pp. 58-78, 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024, Auckland, New Zealand, 6/05/24. https://doi.org/10.1007/978-3-031-70074-3_4

A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. / Buzcu, Berk; Pannatier, Yvan; Aydoğan, Reyhan et al.
Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers: Conference Proceedings. ed. / Davide Calvaresi; Amro Najjar; Andrea Omicini; Rachele Carli; Giovanni Ciatto; Reyhan Aydogan; Joris Hulstijn; Kary Främling. Cham: Springer, 2024. p. 58-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14847 LNAI).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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AB - 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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Buzcu B, Pannatier Y, Aydoğan R, Ignaz Schumacher M, Calbimonte JP, Calvaresi D. A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. In Calvaresi D, Najjar A, Omicini A, Carli R, Ciatto G, Aydogan R, Hulstijn J, Främling K, editors, Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers: Conference Proceedings. Cham: Springer. 2024. p. 58-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-70074-3_4