Modeling culture in intelligent virtual agents

Abstract

This work addresses the challenge of creating virtual agents that are able to portray culturally appropriate behavior when interacting with other agents or humans. Because culture influences how people perceive their social reality it is important to have agent models that explicitly consider social elements, such as existing relational factors. We addressed this necessity by integrating culture into a novel model for simulating human social behavior. With this model, we operationalized a particular dimension of culture-individualism versus collectivism-within the context of an interactive narrative scenario that is part of an agent-based tool for intercultural training. Using this scenario we conducted a cross-cultural study in which participants from a collectivistic country (Portugal) were compared with participants from an individualistic country (the Netherlands) in the way they perceived and Multi-Agent Syst (2016) 30:931-962 interacted with agents whose behavior was either individualistic or collectivistic, according to the configuration of the proposed model. In the obtained results, Portuguese subjects rated the collectivistic agents more positively than the Dutch but both countries had a similarly positive opinion about the individualistic agents. This experiment sheds new light on how people from different countries differ when assessing the social appropriateness of virtual agents, while also raising new research questions on this matter.

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“…Prior studies in computer science note that it is important to consider sociocultural dimensions (i.e., shared knowledge and assumptions in the culture) when designing the cognitive process of virtual agents (e.g., Degens et al 2014). Mascarenhas, Degens, and Paiva (2016) studied whether cultural backgrounds influence how customers evaluate the appropriateness of virtual agents. They designed a collectivist agent and an individualist agent by adapting the way the agent greets the user, asks a personal question, and keeps a relational distance from the user.…”

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“…Reasoner components are used in conjunction to augment the capabilities of the decision making and emotional responses of each agent in different socio-cultural contexts. An example is a reasoner addressing social importance; it allows to create groups of agents that would act and feel according to different cultural values (Mascarenhas et al 2016). A second reasoner, which is named CiF-CK, is based on a model that describes different social exchanges and its consequences within a social environment (Guimaraes et al 2017).…”

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“…[21]) for creating agents that adapt to a user's culture [1]. The agents primarily aim to increase senses of likeability, trust, and empathy with users by emulating the familiar social customs and behaviors based on models of cultural dimensions [28,29,47]. The models may be enough for these purposes, but is not enough to tell us how people would respond to specific feedback and how to best tailor the feedback to increase its effectiveness.…”