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 inter- view 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.

[1]  Philip L. Roth,et al.  Understanding applicant behavior in employment interviews: A theoretical model of interviewee performance , 2011 .

[2]  Marco Gillies,et al.  Customisation and Context for Expressive Behaviour in the Broadband World , 2004 .

[3]  Therese Macan,et al.  The employment interview: A review of current studies and directions for future research , 2009 .

[4]  Brian Ravenet.  A computational model of social attitude effects on the nonverbal behavior for a relational agent , 2012 .

[5]  Judith A. Hall,et al.  Beliefs about the nonverbal expression of social power , 2005 .

[6]  Stefan Kopp,et al.  Towards a Common Framework for Multimodal Generation: The Behavior Markup Language , 2006, IVA.

[7]  Bilge Mutlu,et al.  MACH: my automated conversation coach , 2013, UbiComp.

[8]  P. Costa,et al.  Validation of the five-factor model of personality across instruments and observers. , 1987, Journal of personality and social psychology.

[9]  Stefan Kopp,et al.  The Next Step towards a Function Markup Language , 2008, IVA.

[10]  Dirk Heylen,et al.  First Impressions: Users' Judgments of Virtual Agents' Personality and Interpersonal Attitude in First Encounters , 2012, IVA.

[11]  Stacy Marsella,et al.  Modeling Side Participants and Bystanders: The Importance of Being a Laugh Track , 2011, IVA.

[12]  Timothy V. Schafer.  Better Game Characters by Design: A Psychological Approach , 2006 .

[13]  A. Beck,et al.  An information processing model of anxiety: automatic and strategic processes. , 1997, Behaviour research and therapy.

[14]  Jerold L. Hale,et al.  Relational Messages Associated with Nonverbal Behaviors. , 1984 .

[15]  Timothy W. Bickmore,et al.  Establishing and maintaining long-term human-computer relationships , 2005, TCHI.

[16]  Todd J. Maurer,et al.  The effect of coaching interviewees on subsequent interview performance in structured experience-based interviews , 2008 .

[17]  John Powell,et al.  How level of realism influences anxiety in virtual reality environments for a job interview , 2013, Int. J. Hum. Comput. Stud..

[18]  Patrick Gebhard,et al.  A Job Interview Simulation: Social Cue-Based Interaction with a Virtual Character , 2013, 2013 International Conference on Social Computing.

[19]  R. McCrae,et al.  An introduction to the five-factor model and its applications. , 1992, Journal of personality.

[20]  Marilyn A. Walker,et al.  Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits , 2011, CL.

[21]  Ken Graap,et al.  Frequency of stuttering during challenging and supportive virtual reality job interviews. , 2006, Journal of fluency disorders.

[22]  Peter Alexander Creed,et al.  Job-seeking and job-acquisition in high school students , 2008 .

[23]  Catherine Pelachaud,et al.  From a User-created Corpus of Virtual Agent's Non-verbal Behavior to a Computational Model of Interpersonal Attitudes , 2013, IVA.

[24]  K. Scherer,et al.  Emotion recognition from expressions in face, voice, and body: the Multimodal Emotion Recognition Test (MERT). , 2009, Emotion.

[25]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.