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Enhancing sentient embodied conversational agents with machine learning
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Within the area of intelligent User Interfaces, we propose what we call Sentient Embodied Conversational Agents (SECAs): virtual characters able to engage users in complex conversations and to incorporate sentient capabilities similar to the ones humans have. This paper introduces SECAs together with their architecture and a publicly available software library that facilitates their inclusion in applications -such as educational and elder-care- requiring proactive and sensitive agent behaviours. In fact, we illustrate our proposal with a virtual tutor embedded in an educational application for children. The evaluation was performed in two stages: firstly, we tested a version with basic textual processing capabilities; and secondly, we evaluated a SECA with Machine-Learning enhanced user understanding capabilities. The results show a significant improvement in users' perception of the agent's understanding capability. Indeed, the Response Error Rate decreased from 22.31% to 11.46% when using ML techniques. Moreover, 99.33% of the participants consider the global experience of talking with the virtual tutor with sentient capabilities to be satisfactory.
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TELLOLS ASENSI, Dolça, LÓPEZ SÁNCHEZ, Maite, RODRÍGUEZ SANTIAGO, Inmaculada, ALMAJANO, Pablo, PUIG PUIG, Anna. Enhancing sentient embodied conversational agents with machine learning. _Pattern Recognition Letters_. 2020. Vol. 129, núm. 317-323. [consulta: 20 de gener de 2026]. ISSN: 0167-8655. [Disponible a: https://hdl.handle.net/2445/192860]