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

by Berk Buzcu, Yvan Pannatier, Reyhan Aydoğan, Michael I. Schumacher, Jean-Paul Calbimonte, and Davide Calvaresi

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.

• URL: https://publications.hevs.ch/index.php/publications/show/3147
• DOI: https://doi.org/10.1007/978-3-031-70074-3_4

@INPROCEEDINGS{buzcu2024framework,
     author = {Buzcu, Berk and Pannatier, Yvan and Aydoğan, Reyhan and Schumacher, Michael and Calbimonte, Jean-Paul and Calvaresi, Davide},
   keywords = {Chatbot Framework, Explainable AI, User Study},
      month = aug,
      title = {A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study},
  booktitle = {In post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems},
       year = {2024},
      pages = {58-78},
  publisher = {Springer Nature Switzerland},
       issn = {1611-3349},
       isbn = {9783031700743},
        doi = {10.1007/978-3-031-70074-3_4}
}