During the past two decades, robots have been increasingly deployed in games. Researchers use games to better understand human-robot interaction and, in turn, the inclusion of social robots during gameplay creates new opportunities for novel game experiences. The contributions from social robotics and games communities cover a large spectrum of research questions using a wide variety of scenarios. In this article, we present the first comprehensive survey of the deployment of robots in games. We organise our findings according to four dimensions: (1) the societal impact of robots in games, (2) games as a research platform, (3) social interactions in games, and (4) game scenarios and materials. We discuss some significant research achievements and potential research avenues for the gaming and social robotics communities. This article describes the state of the art of the research on robots in games in the hope that it will assist researchers to contextualise their work in the field, to adhere to best practices and to identify future areas of research and multidisciplinary collaboration.

Current architectures for social agents are designed around some specific units of social behavior that address particular challenges, such as modeling beliefs and motivations, establishing social relationships, or understanding group memberships. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social reality as an interpretation of the physical world with social meanings and selective deployment of cognitive resources adequate to the situation. We identify several design principles that should be considered while designing agent architectures for socio-cognitive systems. Taking these remarks into account, we propose a socio-cognitive agent model based on the concept of cognitive social frames that allow the adaptation of an agent’s cognition based on its interpretation of its surroundings, its social context. Our approach supports an agent’s reasoning about other social actors and its relationship with them. Cognitive social frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of social identity.

Individuals change who they are in response to their social environment. In other words, one's identity is dynamic, varying according to context (e.g., individuals present, place, task). Identity has a significant impact on an individual's behaviour. Researchers have been interested in understanding how contextual aspects shape identity and, in turn, how identity influences behaviour. Agent-based simulation models are great tools to identify and predict behaviour associated with these identity processes. In addition, agents can employ identity-related mechanisms based on social theories to become more socially believable and similar to humans. The Social Identity Approach (SIA) is one of the most influential theories covering social aspects of one's identity, with many of its concepts being applied in social simulation research. This paper formalizes the Dynamic Identity Model for Agents (DIMA), an existing agent-based model based on SIA, providing a detailed theoretical foundation of the model, as well as an overview of its integration as a component into a social agent architecture. In DIMA, agents perceive themselves either as distinct individuals (personal identity) or as members of a social group (social identity), acting according to their context-dependent active identity. Two simulation scenarios are presented here to illustrate the use of this model, one based on the Dictator Game and the other on a trash collection task. This work aims to guide other researchers who want to enhance their agents with the DIMA's identity salience mechanism. As a result, they would not only be able to assess how this mechanism influences behaviour based on the context, but they would also be able to explore the dynamics between personal and social identities.

To be considered socially intelligent, agents must be able to adjust their behavior to different social contexts. However, those adjustments must be recognized and understood by other agents and humans, particularly to identify the social motivations that triggered such changes. In this work, we report on an experimental study that explores how external observers attribute social motivations to behavior and appearance changes that agents make to adapt to the context. Furthermore, we study how the presence of other agents in the context might impact the attribution of motivations to specific changes. Our results indicate that participants identify agents’ appearance changes as more socially and spatially motivated than behavioral changes. Additionally, agents’ awareness is rated higher when the agents adjust their physical characteristics compared to behavioral changes.