A Computational Model of Social Attitudes for a Virtual Recruiter

This paper presents a computational model of social attitude for virtual agents. In our work, the agent acts as a virtual recruiter and interacts with a user during job interview training. Training sessions have a predefined level of difficulty, which is used along with the perceived user's anxiety at each speaking turn to compute the objectives of the recruiter, namely to challenge or comfort the user. Given an objective, the recruiter chooses how to conduct the interview (i.e. the complexity of its questions), and which social attitudes to express toward the user. Social attitudes are defined along 2 dimensions, dominance and liking. Our model computes both the verbal and nonverbal behaviors of the virtual agent to express a given social attitude. A study on the perception of the attitude of the virtual recruiter endowed with our model has been conducted. We show how the different verbal and non-verbal behaviors defined to either challenge or comfort human interviewees enable the virtual recruiter to successfully convey social attitudes.