Towards Social Identity in Socio-Cognitive Agents

Current architectures for social agents are designed around some specific units of social behaviour that address particular challenges. 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

Integrating social power into the decision-making of cognitive agents

AbstractSocial power is a pervasive feature with acknowledged impact in a multitude of social processes. However, despite its importance, common approaches to social power interactions in multi-agent systems are rather simplistic and lack a full comprehensive view of the processes involved. In this work, we integrated a comprehensive model of social power dynamics into a cognitive agent architecture based on an operationalization of different bases of social power inspired by theoretical background research in social psychology. The model was implemented in an agent framework that was subsequently used to generate the behavior of virtual characters in an interactive virtual environment. We performed a user study to assess users' perceptions of the agents and found evidence supporting both the social power capabilities provided by the model and their value for the creation of believable and interesting scenarios. We expect that these advances and the collected evidence can be used to support the development of agent systems with an enriched capacity for social agent simulation

EEG Mode: emotional episode generation for social sharing of emotions

Social sharing of emotions (SSE) occurs when one communicates their feelings and reactions to a certain event in the course of a social interaction. The phenomenon is part of our social fabric and plays an important role in creating empathetic responses and establishing rapport. Intelligent social agents capable of SSE will have a mechanism to create and build long-term interaction with humans. In this paper, we present the Emotional Episode Generation (EEG) model, a fine-tuned GPT-2 model capable of generating emotional social talk regarding multiple event tuples in a human-like manner. Human evaluation results show that the model successfully translates one or more event-tuples into emotional episodes, reaching quality levels close to human performance. Furthermore, the model clearly expresses one emotion in each episode as well as humans. To train this model we used a public dataset and built upon it using event extraction techniques(1).info:eu-repo/semantics/publishedVersio

Building Persuasive Robots with Social Power Strategies

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change

Game Mechanics for Cooperative Games

In this paper, we approach the subject of Cooperative Video Games and their Design. We start out by examining Cooperative Game Mechanics - these include common Design Patterns used currently in Cooperative Video Games and how the challenge archetypes are currently used in Cooperative Video Games. We then proceed to examine our experience in designing a cooperative two player video game using the previously mentioned patterns and challenges, and we present some preliminary evaluation data of the game

An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper

This short paper presents an architectural overview of an agent-based framework called iv4XR for automated testing that is currently under development by an H2020 project with the same name. The framework's intended main use case of is testing the family of Extended Reality (XR) based systems (e.g. 3D games, VR sytems, AR systems), though the approach can indeed be adapted to target other types of interactive systems. The framework is unique in that it is an agent-based system. Agents are inherently reactive, and therefore are arguably a natural match to deal with interactive systems. Moreover, it is also a natural vessel for mounting and combining different AI capabilities, e.g. reasoning, navigation, and learning

ISPO: a serious game to train the interview skills of police officers

The training of Police Interview competencies relies on the hiring of actors to play the role of victims, witnesses and suspects. While role-play can be a particularly effective training technique, it requires a significant amount of resources. The Interview Sim-ulation for Police Officers (ISPO) is a serious game developed as a collaboration of Gameware Europe with the Portuguese School of Police Officers. The objective of the game is to train police officers in communication competencies related to the interview of victims, witnesses, and suspects. Through ISPO, players can take the role of a police interviewer and practice the techniques and methodologies learned in theoretical classes. The serious game offers a safe, lightweight and easily repeatable experience. In order to evaluate the training effectiveness of the serious game, a study was con-ducted with 194 participants where general subjective learning effectiveness was mea-sured. Overall, the ISPO game improved the self-perceived competence of its players. Additionally, participants changed their opinion regarding the most valuable attitudes necessary to conduct a successful interview. Finally, the interaction with the game had a stronger effect on inexperienced users. These results lead us to believe that ISPO can be an added value to police officer schools.info:eu-repo/semantics/publishedVersio