# name
Enhancing sentient embodied conversational agents with machine learning

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.ispartof[].value
Pattern Recognition Letters, 2020, vol. 129, p. 317-323

# metadata.dc.source[].value
Articles publicats en revistes (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Enhancing sentient embodied conversational agents with machine learning