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_28: This article discusses a formal belief, desire, intention (BDI)-based agent model for theory of mind (ToM). The model uses BDI concepts to describe the reasoning process of an agent that reasons about the reasoning process of another agent, which is also based on BDI concepts. We discuss three different application areas and illustrate how the model can be applied to each of them. We explore a case study for each of the application areas and apply our model to it. For each case study, a number of simulation experiments are described, and their results are discussed.
  @cite_18: We report the implementation and evaluation of a Simulation Theory (ST) approach to the Theory of Mind in intelligent graphical agents driven by an affective agent architecture FAtiMA. The existing cognitive appraisal mechanism is adapted to produce a second appraisal cycle, a double appraisal, in order to evaluate the emotional impact of possible actions. The action with the greatest emotional impact is selected as a means of producing more interesting dramatic actions. A variant in which the actual minds of characters present are used is also implemented and evaluated. Results show that these mechanisms do produce more interesting stories.
  @cite_1: We examine the effectiveness of an agent’s approximate theory of mind when interacting with human players in a wartime negotiation game. We first measure how accurately the agent’s theory of mind captured the players’ actual behavior. We observe significant overlap between the players’ behavior and the agents’ idealized expectations, but we also observe significant deviations. Forming an incorrect expectation about a person is not inherently damaging, so we then analyzed how different deviations affected the game outcomes. We observe that many classes of inaccuracy in the agent’s theory of mind did not hurt the agent’s performance and, in fact, some of them played to the agent’s benefit. The results suggest potential advantages to giving an agent a computational model of theory of mind that is overly simplified, especially as a first step when investigating a domain with as much uncertainty as wartime negotiation.
  @cite_17: Computerspellen worden steeds vaker gebruikt om mensen te trainen. Zulke virtuele trainingsspellen worden bijvoorbeeld ingezet voor het trainen van leiderschap, onderhandelen en sociale vaardigheden. De trainee krijgt in een trainingssessie meestal te maken met een of meerdere virtuele teamleden, collega’s of tegenstanders. Om van virtuele training te leren is het belangrijk dat deze virtuele personages realistisch menselijk gedrag vertonen. Er zijn twee manieren om dat te bereiken. Ten eerste kunnen mensen de virtuele personages aansturen, bijvoorbeeld instructeurs of medestudenten van de trainee. Ten tweede kunnen intelligente software agenten het gedrag van de virtuele personages automatisch genereren. Een voordeel van het gebruik van intelligente agenten in plaats van menselijke spelers is dat trainees zo zelfstandig kunnen trainen, waar en wanneer ze maar willen.
summary: Existing computational ToM models either imply a choice between the TT and ST theories ( @cite_18 that relies on a ST approach, or @cite_28 and @cite_1 that position in the TT) or implement them separately as in @cite_17 . In our work, we propose a hybrid approach that relies on theory-theory to model the agent's mental states and commonsense rules, but also on simulation-theory to others' perspective by projecting attributed mental states on its own inference engine. Both models are integrated in the same reasoner.