State-of-the-art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a labourious authoring task as it is necessary to manually define behaviour rules and create content for different interaction scenarios. While Socially Intelligent Agents (SIAs) research has centred on the user experience, we shift focus to the authors. To understand the challenges faced by authors who create these agents, we performed an innovative analysis of the authoring experience in modern agent modelling tools. One key finding is that, while SIA concepts are generally understandable, emotional-based concepts are not as easily comprehended or used by authors. We propose a hybrid solution approach that culminated in the development of Authoring-Assisted FAtiMA-Toolkit. The augmented agent modelling tool incorporates a data-driven Authoring Assistant to boost author productivity while promoting transparency and authorial control. To evaluate the impact of this framework on the authoring experience, we conducted a user study. Results showed that authors using the Authoring-Assisted FAtiMA-Toolkit were on average able to create more SIA-related content in less time. Our findings suggest that data-augmented, theory-grounded agent modelling tools can support the development of affective social agents by reducing the authoring burden without sacrificing the framework’s clarity or the authors’ control over the content.

Psychology-oriented research extensively studied how affect influences human decision making. Particularly, the cognitive tuning assumption suggests that mood can serve to regulate between shallow and deliberative decisions – a more negative mood means higher deliberation. This work proposes a model that mimics the cognitive tuning assumption. To that end, the Collective Risk Dilemma (CRD) gameFor the Planetwas designed and created, a process allowing for distinct levels of reasoning was defined and tested in that game, and mood was integrated to control the level of reasoning of such a process. Several distinct Artificial Intelligence (AI) profiles were created to verify that the developed model was able to dynamically change the level of reasoning and adjust the resulting AI behavior in our CRD game. Results revealed that the distinct AI profiles were influenced by their affective states and experienced circumstances, and that the emergent behaviors were consistent with the cognitive tuning assumption, thus demonstrating that we managed to construct an innovative and flexible model that can use mood to dynamically adjust the level of reasoning of an AI agent.

Traditionally, video-game maps are either made by hand, requiring many man-hours to produce good results, or made using Procedural Content Generation (PCG) techniques, which rely on a predetermined algorithm to generate every feature of the map. More recent studies have tried an approach using Deep Learning algorithms, which have their own limitations, in particular taking away the creative freedom of the designers. To circumvent this problem we propose a system that transforms low fidelity sketches into realistic height-maps through a Deep Learning model we call the Dual Critic Conditional Wasserstein GAN (DCCWGAN), thus providing high visual quality without removing control from the user. The presented system is capable of producing images that resemble the received input, and a user study with 79 participants showed that observers are not able to distinguish between earth-based height-map images and the images generated by our system.

Current Higher Education Institutions' mental health support systems lack the capabilities to cope with the growing need and demand for mental health support from students. We introduce MHeVA -- Mental Health Virtual Assistant -- which was designed with the goal of creating an intelligent virtual agent that could serve as a first-line diagnostic-aid tool for mental health services across universities and faculties. Students interact with the agent which attempts to establish rapport and promotes disclosure through mental health state evaluation questions. In addition to this, MHeVA has the ability to assess self-reported anxiety levels, provide health improvement tips and flag the most severe cases.

Socially Intelligent Agents (SIAs) have become increasingly popular in various contexts, including education and entertainment. However, creating complex social scenarios tailored to a designer's specific goals remains a significant challenge. The authoring burden can be substantial, limiting the potential of SIAs to deliver rich, engaging experiences. In this work, we propose leveraging the extensive knowledge stored within Large Language Models and use theory-driven prompting to extract social practices and identify appropriate social affordances for a scenario description. Our prompting approach aims to guide the system into considering the essential components (beliefs and desires) necessary to produce intentions, actions, and emotions1. Results show that our approach produces large amounts of accurate and new information that can add value to the scenario. However, the process can introduce inaccuracies without human supervision.

Friendships are a fundamental source of support during challenging times, especially among pre-adolescents. The current pandemic situation makes it even harder to rely on support from their peers or strengthen friendships. To accompany and support pre-adolescents outside of school at a moment where most interactions happen online, we propose the HUB, a novel online interactive social space. The HUB is a safeguarded and monitored social space which seeks to improve social well-being and positive reinforcement practices between peers by design. This paper’s key contributions derived from designing the HUB are threefold: an online social space which follows an iterative user-centered design approach; it is grounded on a theoretical model of friendship development to scaffold interactions of dyadic relationships that occur on the HUB; and it employs a set of gamification strategies, such as quests, achievements and rewards to keep pre-adolescents motivated, and, particularly, an acknowledgement system that encourages peers to work on, and acknowledge, character strengths and social skills in others, which are fundamental for their development as individuals.

Style and emotional expressiveness are essential aspects of virtual character computer animation. For a virtual character to display different emotions, motion capture data conveying each desired style has to be recorded, even if the baseline motion is the same. Animators then have to refine and conjoin each recording in order to create the final animations making it a timely and costly process. Although there have been efforts made into the automatic generation of motions, the problem persists that, for each new desired emotion, reference data displaying said emotion has to be readily available and a new motion has to be learned from scratch. By combining Machine Learning with Emotion Analysis - in particular Laban Movement Analysis and the Pleasure, Arousal, Dominance Emotional State Model - we have developed a system that is capable of not only identifying the perceived emotion of locomotion animations but that also allows users to alter the character’s expressed emotion in real time and without the need of additional data.

Early adolescence is a time of major social change; a strong sense of belonging (SB) and peer connectedness is an essential protective factor in mental health (MH) during that period. In this paper we introduce LINA, an augmented reality (AR) smartphone-based serious game played in school by an entire class (age 10+) together with their teacher, which aims to facilitate and improve peer interaction, SB and class climate, while creating a safe space to reflect on MH and external stressors related to family circumstance. LINA was developed through an interdisciplinary collaboration involving a playwright, software developers, psychologists and artists, via an iterative co-development process with young people. A prototype has been evaluated quantitatively for usability and qualitatively for efficacy in a study with 91 early adolescents (agemean=11.41). Results from the Game User Experience Satisfaction Scale (GUESS-18) and data from qualitative focus groups showed high acceptability and preliminary efficacy of the game. Using AR, a shared immersive narrative and collaborative gameplay in a shared physical space offers an opportunity to harness adolescent affinity for digital technology towards improving real-world social connection and SB.

More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this article, we describe the FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers, and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects, and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, the FAtiMA Toolkit’s library-based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components, and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of the FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.

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 techniques1.

Throughout the years, social norms have been promoted as an informal enforcement mechanism for achieving beneficial collective outcomes. Among the most used methods to foster interactions, framing the context of a situation or setting in-game rules have shown strong results as mediators on how an individual interacts with their peers. Nevertheless, we found that there is a lack of research regarding the use of incentives such as scores to promote social interactions differing in valence. Weighing how incentives influence in-game behavior, we propose the use of rewards to promote interactions varying in valence, i.e. positive or negative, in a two-player scenario. To do so, we defined social valence as a continuous scale with two poles represented by Complicate and Help. Then, we performed user tests where participants where asked to play a game with two reward-based systems to test on whether the scoring system influenced the social interaction valence. The results indicate that the developed reward-based systems were able to foster interactions diverging in social valence scores, providing insights on how factors such as incentives overlap individual’s established social norms. These findings empower game developers and designers with a low-cost and effective policy tool that is able to promote in-game behavior changes.

In this work we test the hypothesis that interacting with an intelligent virtual character in Virtual Reality (VR) has a stronger impact compared to the same interaction in a traditional non-immersive platform, both in terms of presence and believability.

In this paper, we propose a feature-based model to recognize emotions via touching patterns of individuals playing a game on a typical tablet. In this work, novel features, such as Angular Velocity/Acceleration, Angle, Curl, Area and number of strokes within a time window, are introduced and the gold-standard of the data is determined automatically via subjects' facial expressions. The results show that the approach is promising and the model is able to recognize all the six basic emotions, with a performance of 71.92 % ±0.51. In addition, the recognition of valence and arousal reaches correlation coefficients equal to 0.76 and 0.78 respectively.

Serious games are moving towards multiplayer environments which aim to concurrently train and educate people. Therefore, we believe there is a growing need to construct systems which account for this tendency in order to ease learning and training. We here propose a model named Group Interactions Manager for Multiplayer sErious games (GIMME) which aims to improve the collective ability of players interacting in environments such as these. Our method organizes the players in groups and computes the types of interactions that should be promoted for each group. These interactions are then promoted by generating adequate game mechanics. We validate the most important aspects of the model by performing several agent-based simulations. The simulations suggest that learning can be improved when applying our strategy, as the average ability of the agents rapidly converged to high, near optimal values, as opposed to a random baseline (a strategy possibly implemented by real teachers when they do not know the students) which maintained low values. Moreover, unlike the random strategy, GIMME considerably approximated the promoted interactions profiles to the agents’ computed preferences.

Even though modern video games present rich storytelling and high graphical fidelity, they are still lacking in rich non-scripted NPC social interactions. In this work we present an extension of the Comme il Faut (CiF) and CiF-CK social interaction models, where we added emotions and a emotion appraisal process based on the OCC model of emotion and also added a belief system that models the social network values that the NPCs expect regarding the other NPCs' relationships. A version of the new model, which we named Comme il Faut - Exiles (CiF-Ex), was implemented in the AAA game Conan Exiles using their modding tools and validated. The results were noteworthy: the users enjoyed the game more and tended to spend more time near CiF-Ex enabled NPCs. The majority of the users preferred CiF-Ex NPCs, versus the vanilla NPCs. CiF-Ex NPCs were perceived as more believable and less predictable.

Is a robot that shares explicitly its emotions with users more believable and friendly? In a previous study addressing this question, results suggested that an emotion sharing feature in a robot may have negative effects in the perception of that robot. Here, we address the same question but also take into account the “competence” of a robot executing a task, to understand if some kind of interaction occurs.

For more than a century, empathy has been a central topic in the study of human emotion. It plays a crucial role in our everyday social life, having implications for the survival of the species. In the case of agents that inhabit virtual worlds and interact socially among each other and with humans, empathy has also been considered to be an important mechanism to promote engaging and believable interactions. However, creating empathic agents, until recently, has been accomplished mostly through the implementation of specific empathic behaviors or by using domain-dependent empirical models. In this article, we propose a generic computational model of empathy that is grounded on recent psychological theories about empathy. The proposed model treats empathy as a process in which the intensity of the empathic response is modulated by a set of factors that involve the relationship between the agents of the empathic interaction, namely, the similarity and affective link, as well as some characteristics of the empathizer agent, such as mood and personality. This model was implemented into an affective agent architecture, which was then used in an evaluation that had 77 participants. The results indicate that our empathy model, when used to simulate a social scenario with a small group of agents, significantly changed the way that the users perceived and described the interactions between those agents.