In this article we describe a general cognitive model of human social behavior that is meant to increase the social intelligence of autonomous intelligent agents in different contexts. Despite the remarkable improvements that have been made on human-agent interaction, agents still have a limited capacity to be aware of the social reality that is present in the human mind and significantly guides human behavior. The model discussed in this paper is a step toward increasing that capacity significantly. Two different case studies are described in which the proposed model is used to better explain and predict human behavior. The first case study is the well known Ultimatum game. The second one is a variation of the “Game of Nines” played by children.

Emotional synthetic characters are able to evaluate (appraise) events as positive or negative with their emotional states being triggered by several factors. Currently, the vast majority of  models for appraisal in synthetic characters consider factors related to the goals and preferences of the characters. We argue that appraisals that only take into consideration these "personal" factors are incomplete as other more social factors, such as the normative and the social context, including in-group and out-group relations, should be considered as well. Without them, moral emotions such as shame cannot be appraised, limiting the believability of the characters in certain situations. We present a model for the appraisal of characters' actions that evaluates whether actions by in-group and out-group members which conform, or not, to social norms generate different emotions depending on the social relations between the characters. The model was then implemented in an architecture for virtual agents and evaluated with humans. Results suggest that the emotions generated by our model are perceived by the participants, taking into account the social context and that participants experienced very similar emotions, both in type and intensity, to the emotions appraised and generated by the characters.

Artificial Intelligence (AI) and Personalization are both essential - How do we relate content (the factual knowledge aspects of all games, be they serious or entertainment contained, game mechanics) and context (experiences based. In this research the role of AI and Personalization is and activities) to pedagogical goals towards supporting however focused upon the context of Serious Games (SG) in pedagogically-driven design and development of SGs? particular. A concerted research direction is necessary in this From these two high-level questions we derived a more area so as to establish future benchmarks and metrics for the pragmatic approach to AI and Personalization based on: In effective use of AI and Personalization in serious games design what ways can personalization improve learning and adapt and will benefit relevant research communities in providing best to learner requirements?