TY  - CONF
T1  - A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study
A1  - Buzcu, Berk
A1  - Pannatier, Yvan
A1  - Aydoğan, Reyhan
A1  - Schumacher, Michael
A1  - Calbimonte, Jean-Paul
A1  - Calvaresi, Davide
TI  - In post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems
Y1  - 2024
SP  - 58
EP  - 78
PB  - Springer Nature Switzerland
SN  - 9783031700743
SN  - 1611-3349
M2  - doi: 10.1007/978-3-031-70074-3_4
KW  - Chatbot Framework
KW  - Explainable AI
KW  - User Study
N2  - 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.
ER  -