In this paper, we present multimodal computational models of interactional trust in a humans-robot interaction scenario. We address trust modeling as a binary as well as a multi-class classification problem. We also investigate how early- and late-fusion of modalities impact trust modeling. Our results indicate that early-fusion performs better in both the binary and multi-class formulations, meaning that modalities have co-dependencies when studying trust. We also run a SHapley Additive exPlanation (SHAP) values analysis for a Random Forest in the binary classification problem, as it is the model with the best results, to explore which multimodal features are the most relevant to detect trust or mistrust.
CITATION STYLE
Hulcelle, M., Varni, G., Rollet, N., & Clavel, C. (2023). Computational Multimodal Models of Users’ Interactional Trust in Multiparty Human-Robot Interaction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13643 LNCS, pp. 225–239). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37660-3_16
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