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 | |
| Adicionado por: | [] |
| Total mark: | 0 |
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