Cyberbullying is a pressing issue in today’s society due to advancing technology and increasing reliance on it. To tackle this problem, we created a social simulation serious game aimed at teachers as a visual novel format. As the player, the teacher navigates a school populated with social agents, all taking place on an online platform designed for a blended learning teacher training program. This approach aims to assess the value of gamification in shaping formative content and improving teacher training related to cyberbullying. Following encouraging testing and validation, we developed editing tools allowing to extend the game and platform, enabling the creation of tailored cyberbullying training programs with various emotional elements, suited to various school cultures or alternative subjects. This paper presents our serious game and online platform.

Although game design and adaptation are already challenging in single-player games due to the uncertainty of human behaviour, they become even more complex (but also more interesting) in multiplayer games given the human interaction they entail. Game research usually values how subjects with different characteristics perceive rewards and vary their conduct in single-player or competitive settings. The present study complements this by assessing how a player’s predisposition to share rewards in a cooperative scenario varies with their Agreeableness and Extraversion, as measured with the Five-Factor Model of personality. To this end, a two-player action role-playing game with a loot system named Cast Away was developed, and the actions of multiple pairs of players (N=40) were recorded. The behaviour and experience of different player personalities were tested, and a moment-by-moment analysis was conducted for game circumstances such as piking up potions or when a weapon was shared. Results support that the personality of another player has a greater impact on one’s behaviour and experience than their own, which was unexpected and challenges common multiplayer game design.

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.

Although multiple models have been proposed to explain what motivates different players to engage with digital games, the domain of tabletop games have not received the same attention from the research community. This work tries to fill that gap, investigating what drives us to play tabletop games. Based on the existing literature and in conjunction with tabletop game experts, we designed a questionnaire capturing relevant concerns both related to the analog artifact itself as well as to the context of play. Through principal component analysis of the responses from a diverse range of tabletop game hobbyists (N = 229, with an average collection of 99 tabletop games each), we propose a model with 5 dimensions characterizing the main motivations to play tabletop games that explains 80% of the variance of the data: (C)ompetitive interaction, (S)ocial challenge, (S)ensory experience, (I)ntellectual challenge, and (I)maginative experience. We discuss each dimension and compare our results to existing models on play motivations, and propose a 3-component version of our model providing a bridge to a broader interaction model. By understanding why players engage with tabletop games in their physical form, we hope to help the development of hybrid or digital versions of these games.

Having believable interactions between synthetic characters and with people is crucial to the creation of immersive interactive experiences. The believability of such experiences is strongly connected to having the characters express emotions with the correct timing throughout their interaction while retaining the human participants’ agency. Unfortunately, most video games adopt static animations, which usually only convey a single emotion throughout, making their expression look artificial, or allowing for greater emotion expressiveness but sacrificing the player’s agency. With this work, we propose the creation of a model based on the principles of animation and anticipation that allows for emotion expression during gameplay while retaining the participants’ agency. We evaluate our approach using three models. In the first model, characters express emotions in reaction to what happens (this is similar to how games approach emotion expressions), in the second in anticipation of what could happen, and in the third in reaction to what happens given what was anticipated. We tested these models in Adfectus, a 3D arena fighting game, and found improvements in some dimensions relating to the believability of interactions in particular player demographics.

To better understand how emotions are recognized, we propose the Triad Affect Interpretation (TAI) method that creates a model of how users perceive animations of emotions of specific synthetic characters, with the additional intent of improving the way emotions are communicated so that they are more easily recognized. The method is divided into two main steps and focuses on a set of animations of emotions taken from the synthetic characters under development. The first step aims to elicit meaningful constructs through content analysis. The second step asks participants to rate the same animations against the selected constructs. Using principal component analysis and cluster analysis, we then create a model of the relevant factors to the perception of emotions that can inform future improvements of the animations related to the expression of emotions. The results of a user study suggest that our characters' expressions of Anger and Disgust are confused with each other due to a similar contraction of the face; and Fear and Surprise are confused due to a similar expansion of the face, which provides specific directions for improvements.

When designing interactive scenarios that depend on emotion expression, it is imperative to consider the levels of recognition associated with said expressions, to ascertain whether or not an acceptable degree of emotional communication has been achieved. In this work, two experiments were conducted with that aim, one asking participants to compare two different versions of an application side-by-side when conveying a specific emotion, and another asking the participants to recognize the emotion being expressed in each version. We found that, for some emotions, the approach rated higher in terms of emotion expression during the side-by-side comparison would not translate to the approach with a higher emotion recognition in the second experiment. Although this discrepancy is generally consistent with what happens with emotion recognition in humans, it is noteworthy that some higher-rated choices ended up not being as effective in the expression of emotion. We discuss how these discrepancies might have originated from forced-choice and feature dominance, and why context should be taken into account when designing experiments.

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.

The behaviour of a person in a given situation can be explained understanding his personality traits. In this sense, the identification of these traits can be a great value to achieve personalised social influence. Although there are several models of persuasion, few of them take into account the person's personality traits. For this reason, this work describes a persuasion study that takes into account a person's personality. We develop a storytelling decision-making scenario, where the participant receives influencing messages to follow a pattern of behaviour determined by a persuasive agent (an autonomous social robot with assertive behaviour). From the study, we find evidence that the model used within the proposed scenario managed to make participants more engaged in the activity. We found pieces of evidence that the levels of assertiveness of a person can influence their attitude and perception by an agent. Also, we identify that persuasion strategy which uses persuasive arguments are more efficient than strategies that have no arguments. Finally, our proposed persuasion strategies have achieved a good level of successful influence.

Long distance learning has always faced several challenges in regards to motivation and availability of opportunity for students. While the latter has seen efforts in terms of collaborative and non-traditional learning avenues, such as online courses and virtually hosted classrooms, the former still suffers due to external influences and responsibilities, which in turn lead to an increase in student's disinterest levels. To combat these hindrances, we propose the creation of an intelligent virtual agent based on the Social Regulatory Cycle, to fill the role of a coach in a readily available mobile application. This virtual coach is capable of supporting and adapting to the needs of human students, helping them in their tasks based on their intentions, motivations and feelings. By gathering objective and subjective user data, the virtual coach is able to interpret learning situations of individual users and, with the use of a synthetic character with analogous mannerisms to those of an actual human, help them by modulating their emotional state in the context of the learning process. Preliminary evaluations performed within an online learning environment class showed users continued interest in engaging with the virtual coaches on a regular basis.

This paper addresses the problem of improving the player experience in single player endless games and encouraging the player to be engaged with procedurally generated content for longer periods of time. We present a model in which the procedural content generation process takes into consideration the dynamics of two dimensions of the player experience: the performance of the player when overcoming the challenges created by the game, and the variety of challenges presented to the player over time. We discuss the implementation of the model in the endless mode of the mobile game Smash Time, and describe how its evaluation supports that the model was able to increase both the number and duration of play sessions as well as having a better game experience reported by the participants, when compared to the original game. These results suggest that this approach could improve replayability and, as a consequence, the lifetime of a digital game.

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.

Procedural Content Generation (PCG), i.e. how game content can be created algorithmically, is an increasingly important area and currently one of the most active topics within the software games industry and game research. One of the crucial aspects of PCG is the capacity to maintain the player engaged and in flow. In this work, we explore how player skill progression could be used by PCG to create more appropriate challenges for each player and propose a model for content adaptation that takes this concept as its core feature. Our approach introduces the player as an active element in the adaptation process and assumes both the player and the game should have an equal and active role in this process. Our adaptation explores how modelling the evolution of multiple dimensions of a same challenge while the game is played helps creating a better game experience for the player. To evaluate our approach, we present a novel validation process embedded in the game itself, with the purpose of providing a more direct and seamless way to analyse player preference. The results of the evaluation of our approach in the context of an endless running side-scrolling platformer game revealed that players have consistent and specific preferences regarding how difficulty should evolve over the course of a game, which should be taken into account when designing an engaging game progression.

Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children’s nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children’s affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user’s valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.

In traditional supply chain (SC), planning problems are usually considered individually at each SC entity. However, such decisions often influence the other members in the chain and thus an integrated approach should be considered. By modelling system-wide SC networks, different SC problems, like production planning, coordination, order distribution, among others, can be integrated and solved simultaneously so that the solution is beneficial to all entities in a longterm base. In an attempt to make progress in this area, researchers use various methods for modelling the dynamics of SCs. In the literature review, due to their distinctive characteristics, multi-agent-based systems have emerged as one of the most adequate modelling tools for tackling various aspects of SC problems. In this work, a multi-agent supply chain system (MASCS) model that integrates different SC processes is presented. The proposed model allows modelling different SCs with multi-products and different operational policies considering information asymmetry and distributed/decentralized mode of control. In this article the details of the MASCS model development and implementation are presented. Furthermore, the applicability of the proposed MASCS is briefly demonstrated through the solution of a SC example. The obtained results are discussed and research extensions are outlined.

The idea of autonomous social robots capable of assisting us in our daily lives is becoming more real every day. However, there are still many open issues regarding the social capabilities that those robots should have in order to make daily interactions with humans more natural. For example, the role of affective interactions is still unclear. This paper presents an ethnographic study conducted in an elementary school where 40 children interacted with a social robot capable of recognising and responding empathically to some of the children's affective states. The findings suggest that the robot's empathic behaviour affected positively how children perceived the robot. However, the empathic behaviours should be selected carefully, under the risk of having the opposite effect. The target application scenario and the particular preferences of children seem to influence the degree of empathy that social robots should be endowed with.

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.

The Inter-ACT (INTEracting with Robots - Affect Context Task) corpus is an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. It contains videos that capture the interaction from different perspectives and includes synchronised contextual information about the game and the behaviour displayed by the robot. The Inter-ACT corpus is mainly intended to be a comprehensive repository of naturalistic and contextualised, task-dependent data for the training and evaluation of an affect recognition system in an educational game scenario. The richness of contextual data that captures the whole human-robot interaction cycle, together with the fact that the corpus was collected in the same interaction scenario of the target application, make the Inter-ACT corpus unique in its genre.

Given the recent advances in robot and synthetic character technology, many researchers are now focused on ways of establishing social relations between these agents and humans over long periods of time. Early studies have shown that the novelty effect of robots and agents quickly wears out and that people change their attitudes and preferences towards them over time. In this paper, we study the role of social presence in long-term human-robot interactions. We conducted a study where children played chess exercises with a social robot over a five week period. With this experiment, we identified possible key issues that should be considered when designing social robots for long-term interactions.

In this paper we describe a robotic game buddy whose emotional behaviour is influenced by the state of the game. Using the iCat robot and chess as the game scenario, an architecture for incorporating emotions as a result of a heuristic evaluation of the state of the game was developed. The game buddy was evaluated in two ways. First, we investigated the effects of the characterpsilas emotional behaviour on the userpsilas perception of the game state. And secondly we compared a robotic with a screen based version of the iCat in terms of their influence on userpsilas enjoyment. The results suggested that userpsilas perception of the game increases with the iCatpsilas emotional behaviour, and that the enjoyment is higher when interacting with the robotic version.