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. This paper reports on the development of a multi-agent simulation of small group discussion that focusses on the interaction and the coordination of turn-taking. We describe the addition of nonverbal behaviours, such as gaze, gestures, posture shifts and head and facial expression, to the model; how the agents in the simulation take these behaviours into account in their decisions to speak, to stop, or to give feedback. The simulation is to be evaluated comparing its statistical profile against the statistics generated by a simpler, base model, one without the nonverbal behaviours, to show that it better approximates the statistics of a real group discussion. The properties to be assessed include mean transition intervals, turn lengths, relation of gaze to speaking order, frequency of simultaneous starts, and of feedback. Creating believable behaviors for conversational agents (CAs) is a challenging task, given the complex relationship between speech and various nonverbal behaviors. The two main approaches are rule-based systems, which tend to produce behaviors with limited variations compared to natural interactions, and data-driven systems, which tend to ignore the underlying semantic meaning of the message (e.g., gestures without meaning). We envision a hybrid system, acting as the behavior realization layer in rule-based systems, while exploiting the rich variation in natural interactions. Constrained on a given target gesture (e.g., head nod) and speech signal, the system will generate novel realizations learned from the data, capturing the timely relationship between speech and gestures. An important task in this research is identifying multiple examples of the target gestures in the corpus. This paper proposes a data mining framework for detecting gestures of interest in a motion capture database. First, we train One-class support vector machines (SVMs) to detect candidate segments conveying the target gesture. Second, we use dynamic time alignment kernel (DTAK) to compare the similarity between the examples (i.e., target gesture) and the given segments. We evaluate the approach for five prototypical hand and head gestures showing reasonable performance. These retrieved gestures are then used to train a speech-driven framework based on dynamic Bayesian networks (DBNs) to synthesize these target behaviors. All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.