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A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study
Tipo de publicação: Inproceedings
Citação:
Booktitle: In post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems
Ano: 2024
Mês: August
Páginas: 58-78
Publisher: Springer Nature Switzerland
ISSN: 1611-3349
ISBN: 9783031700743
DOI: 10.1007/978-3-031-70074-3_4
Resumo: 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.
Palavras-chave: Chatbot Framework, Explainable AI, User Study
Autores Buzcu, Berk
Pannatier, Yvan
Aydoğan, Reyhan
Schumacher, Michael
Calbimonte, Jean-Paul
Calvaresi, Davide
Adicionado por: []
Total mark: 0
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  • EREBOTS_Paper_Berk_Davide_Yann...
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