text: Job interview simulation with a virtual agents aims at improving people's social skills and supporting professional inclusion. In such simulators, the virtual agent must be capable of representing and reasoning about the user's mental state based on social cues that inform the system about his her affects and social attitude. In this paper, we propose a formal model of Theory of Mind (ToM) for virtual agent in the context of human-agent interaction that focuses on the affective dimension. It relies on a hybrid ToM that combines the two major paradigms of the domain. Our framework is based on modal logic and inference rules about the mental states, emotions and social relations of both actors. Finally, we present preliminary results regarding the impact of such a model on natural interaction in the context of job interviews simulation.
  @cite_13: Emotion is a cognitive mechanism that directs an agent's thoughts and attention to what is relevant, important, and significant. Such a mechanism is crucial for the design of resource-bounded agents that must operate in highly-dynamic, semi-predictable environments and which need mechanisms for allocating their computational resources efficiently. The aim of this work is to propose a logical analysis of emotions and their influences on an agent's behavior. We focus on four emotion types (viz., hope, fear, joy, and distress) and provide their logical characterizations in a modal logic frame-work. As the intensity of emotion is essential for its influence on an agent's behavior, the logic is devised to represent and reason about graded beliefs, graded goals and intentions. The belief strength and the goal strength determine the intensity of emotions. Emotions trigger different types of coping strategy which are aimed at dealing with emotions either by forming or revising an intention to act in the world, or by changing the agent's interpretation of the situation (by changing beliefs or goals).
  @cite_22: We give a new logic of intention where (contrarily to Cohen&Levesque's approach) intention is a primitive modal operator having a non-normal possible worlds semantics. We then highlight the relation between intention and belief by a set of axioms. In our logic we formulate principles of cooperation allowing an agent to infer new intentions from his beliefs about other agents' intentions. Finally, building on results of linguistic pragmatics, we show that our cooperation principles allow to infer the effects of a yes-no question "Does A hold?" from that of an associated assertive "I have the intention to know whether A". In the same manner requests can be inferred, which form another important subclass of directives. It is the aim of this work to obtain a minimal logic that can be mechanized in a simple way.
summary: To support such adaptability, we propose to rely on the BDI model. Several computational models of emotions have already been proposed (e.g. @cite_22 @cite_13 ) that show that BDI is a good basis to represent and to reason about the interlocutor's mental state. Our aim is thus to define a logical model of emotions and ToM in BDI.