The use of virtual agents in social coaching has increased rapidly in the last decade. In order to train the user in different situations than can occur in real life, the virtual agent should be able to express different social attitudes. In this paper, we propose a model of social attitudes that enables a virtual agent to reason on the appropriate social attitude to express during the interaction with a user given the course of the interaction, but also the emotions, mood and personality of the agent. Moreover, the model enables the virtual agent to display its social attitude through its non-verbal behaviour. The proposed model has been developed in the context of job interview simulation. The methodology used to develop such a model combined a theoretical and an empirical approach. Indeed, the model is based both on the literature in Human and Social Sciences on social attitudes but also on the analysis of an audiovisual corpus of job interviews and on post-hoc interviews with the recruiters on their expressed attitudes during the job interview.
Models of social attitude expression for virtual agents have already been proposed. For instance, in @cite_17 , postures corresponding to a given attitude were automatically generated for a dyad of agents. Ravenet @cite_8 proposed a user-created corpus-based methodology for choosing the behaviours of an agent conveying an attitude along with a communicative intention. The SEMAINE project used ECAs capable of mimicking and reacting to the system user's behaviour to convey a personality. Each of these works used either limited modalities, or a non-interactive (agent-agent) context. Also, none of these works looked at the sequencing of the agent's signal. In this article, we present model of social attitude expression that considers the sequencing of non-verbal behaviour (Section 6).
@cite_8 @cite_17
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Human’s non-verbal behavior may convey different meanings. They can reflect one’s emotional states, communicative intentions but also his social relations with someone else, i.e. his interpersonal attitude. In order to determine the non-verbal behavior that a virtual agent should display to convey particular interpersonal attitudes, we have collected a corpus of virtual agent’s non-verbal behavior directly created by users. Based on the analysis of the corpus, we propose a Bayesian model to automatically compute the virtual agent’s non-verbal behavior conveying interpersonal attitudes. Computer generated characters are now commonplace in television and film. In some media productions like the Matrix™ they feature as frequently as the real cast. A visual media that is being explored by the research community is that of real-time improvisational theatre using virtual characters. This is a non-trivial problem with many research challenges; this paper starts to address one, which is the automatic generation of appropriate non-verbal communication between characters based on their personality and relationship to one another. We focus on our of model interpersonal attitude used for generating expressive postures and eye gaze in computer animated characters. Our model consists of two principle dimensions, affiliation and status. It takes into account the relationships between the attitudes of two characters and allows for a large degree of variation between characters, both in how they react to other characters’ behaviour and in the ways in which they express attitude.