A Framework for Explainable Multi-purpose Virtual Assistants
A Nutrition-Focused Case Study
Berk Buzcu (University of Applied Sciences and Arts Western Switzerland)
Yvan Pannatier (University of Applied Sciences and Arts Western Switzerland)
Reyhan Aydoğan (Özyeğin University, TU Delft - Interactive Intelligence)
Michael Ignaz Schumacher (University of Applied Sciences and Arts Western Switzerland)
Jean Paul Calbimonte (University of Applied Sciences and Arts Western Switzerland)
Davide Calvaresi (University of Applied Sciences and Arts Western Switzerland)
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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.