Contemporary video games offer a plethora of interesting challenges, yet the application of imitation learning in shipped titles remains sparse. One reason is that directly mirroring a player’s setup and behavior can lead to a stale or unfair combat experience where only computer-reaction time or endurance wins the battle. To address this, we investigate a temporally shifted generative imitation learning approach to enhance player-specific adaptation and implicit dynamic difficulty adjustment (DDA). By shifting the imitation model by one iteration of a playthrough, we create an asynchronous confrontation that requires players to think strategically both forwards and backwards in time. Players must make delicate trade-offs: polishing their current build to master the run, while avoiding overtuning an agent they must face in the future. We implemented this in “Dark Zagreus”, a modification of the action roguelike Hades, replacing the final boss with an AI trained on the player’s last successful run. To evaluate its effectiveness, we conducted a two-week user study with 20 participants comparing scripted and imitation-based behaviors. While quantitative metrics showed no significant differences in overall player experience, qualitative feedback highlighted that the AI effectively mirrored individual tactics, and performance analyses revealed accuracy comparable to prior imitation learning approaches. Despite limitations such as omitted game mechanics, this work demonstrates the potential of shifted imitation learning to realize novel, engaging gameplay experiences. The source code is publicly released to foster future research on player-centric AI design.

In the present paper, we set out to understand in what manner character customisation of a video game could have an influence on the mental state of transgender players. Current literature shows that a player’s character can indeed be used to experiment with gender-related struggles in a safe environment. To this end, studies suggest that the character choice of transgender players is based on an alignment with their actual, self-identified gender identity; as a means of both experimentation and wishful identification. However, only limited research has been conducted to find how the portrayal of a video game character’s gender specifically influences the player’s gender dysphoria (i.e., the distress a person feels due to a mismatch between their gender identity and their sex assigned at birth). As such, the present paper contributes (1) an overview of selected background works on the interplay of gender and character customisation, and (2) a qualitative study that includes 37 transgender adolescents between the ages of 16 and 23. Findings of the study extensively discuss to what extent – and to what effect – transgender adolescents use the possibility to customise their character as a means to experiment and/or come to terms with their gender identity.

We present the first experiments from a pilot study with Ruby’s Mission, an applied gaming intervention for reducing feelings of loneliness in children with a chronic illness. Based on a previously performed literature study, Ruby’s Mission sets out to train specific socioemotional skills, through four children sharing emotional experiences. The present study contributes insights in (1) how asymmetrical player roles in a multiplayer game affect in-game social interactions, and (2) if socioemotional skills affect the in-game social interactions in a multiplayer video game. Experiments were conducted during a public event for introducing children to science. A baseline measure of socioemotional skills was made using the emotional awareness questionnaire (EAQ). Video and audio recordings were made during gameplay, and were annotated afterwards on social interaction. As per expectations, results suggest that asymmetrical player roles encourage different levels of social interaction. Analysing the effect of socioemotional skills on an individual player’s in-game social interactions, surprisingly revealed a slight negative effect, where social interactions decreased as socioemotional skills improved. Interestingly, when investigating this effect on per gameplay group level, no interaction was found. Results also reveal that external factors such as parent interference, play environment, and peer relations might have an effect on social interactions as well.

Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player’s emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players’ game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts’ annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games “in the wild” in high-stakes, real-world competitive settings.

Negative body perception can negatively affect one’s health, as it can lead to depression and eating disorders. Body appreciation on the other hand, is about accepting, respecting, loving, and appreciating the body, and has been associated with self-esteem, optimism, life satisfaction, and other factors related to the well-being. The aim of this research project is to investigate whether lower body appreciation can be identified by visualising the discrepancy between someone’s actual self and their ideal self (i.e., the distance between these two domains of the self). This discrepancy is visualised through an avatar creation task using The Sims 4 in which participants have to create an avatar of (1) the way they actually look, and (2) their ideal image. Participants’ body appreciation was measured using the BAS-2 body appreciation scale, which contains ten statements about one’s feelings towards their body. Actual/ideal self-discrepancy was calculated by measuring the number of changes that were made in order to get from the actual body to the ideal body. Analysis of the results reveals that (a) adjustments to certain avatar traits significantly correlate with body appreciation scores, and (b) some gender differences are notably predictive, e.g., with respect to “muscle”, “middle body”, and ”weight” changes. As such, we may conclude that the developed avatar creation task which measures the discrepancy between the actual and ideal self, can indeed be used as a tool to identify (lower) body appreciation.

Children with a chronic disease, such as cystic fibrosis or juvenile arthritis, often face obstacles that can have a negative impact on children’s physical, social-emotional and cognitive development, beyond the actual illness itself. Children with chronic conditions are, on average, lonelier than their peers without such conditions. Feelings of loneliness in children and adolescents have been associated with a wide range of negative outcomes, including school drop-out, depressive symptoms, social anxiety, suicide ideation, low self-esteem, eating disorders, and sleep problems. As such, the present investigation sets out to reduce these feelings of loneliness for children with chronic conditions, and aims to do so by the structured design of an applied gaming intervention. Specifically, the present paper contributes (1) a literature-based understanding on training socioemotional skills as a novel means to reduce feelings of loneliness in chronically ill children, (2) intervention objectives that are aligned to this goal, and (3) a structured proposal for design guidelines that implement the intervention objectives into ‘Ruby’s Mission’; an applied gaming intervention for reducing loneliness of children with chronic illness.

In this study, we used recordings of players’ facial expressions that are captured during competitive Hearthstone games to analyse the correlation between in-game player affective responses and subjective post-game self-reports. With this, we aimed to examine whether eye gaze, head pose and emotions gathered as objective data from face recordings would be associated with subjective experiences of players which were collected in the form of a post-game survey. Data was collected during a live offline Hearthstone competition, which involved a total of 17 players and 31 matches played. Correlation analyses between in-game and post-game variables show that players’ facial expressions and eye gaze measurements are associated with both players’ attention to the opponent and their mood influenced by the opponent. In future research, these results may be used to implement predictive player models.

Frustration is a key concept in retaining a player interest in both commercial and applied games. In a HCI context, frustration is often seen as a purely negative phenomenon. However, for games to be interesting some amount of frustrating has to be present. As such, dynamically adjusting game elements to ensure optimal frustration levels can be a valuable way to increase player retention. A first step towards such a system is an accurate classifier of frustration. To date, most attempts at frustration classification use models that are relatively hard for a human to understand. In this paper an attempt will be made at creating an explainable predictor of player frustration. To accomplish this, the frustration-aggression theory was used to identify a number of key components that determine the severity of a frustrated response. 135 participants were asked to play a series of Pac-Man levels while being asked about the frustration components. Gameplay features, participant behaviour and participant responses were gathered and used as a dataset to train a number of random forest classifiers. The classifiers were trained to predict player frustration, with accuracy ranging from 66.3% to 83.1% depending on the amount of frustration classes used. Accuracy dropped significantly when excluding participant responses on frustration component questions from the dataset. Furthermore, feature importance analysis revealed the overwhelming importance of the Repeated Failures component, as well as the relatively low importance of all in-game variables. These results suggest that the currently used variable set might not accurately represent the components of frustration. A possible avenue for future research could be the discovery of accurate metrics for these internal component perceptions.

In this paper, we study the effect of time pressure on player behaviour during a dilemma-based crisis management game. We employ in-game action tracking, physiological sensor data and self-reporting in order to create multi-modal predictive models of player stress responses during a crisis management scenario. We were able to predict the experimental condition (time pressure vs. no time pressure) with 84.5% accuracy, using a game-only feature set. However, lower accuracy was observed when physiological sensor data was used for the same task. The method presented in this paper can be employed in crisis management training, aiming at assessing players’ responses to stressful conditions and manipulating player stress levels to provide personalised training scenarios.

Increasingly more scholars have regarded the virtual worlds of massively multiplayer online games (MMOGs) as a social laboratory, and have paid research attention to the online interactions between its large number of players. In the present study, we examine a widely observed real-life phenomenon gender homophily (i.e., people of the same gender flocking together) in this virtual context. Specifically, we investigate how collaboration networks in an MMOG (Destiny) are shaped by the adopted gender of the avatar characters. This focus is interesting, as avatar gender in video games is generally a choice that is less constrained than it is in real life. To investigate the effect of gender in online video games, while controlling for the effects of several confounding factors, we employed a technique called exponential random graph modeling. Mirroring how interpersonal relationships are often gendered in real life, and despite common phenomenon such as gender swapping, we found evidence supporting gender homophily in the MMOG environment.

With the constant rise in popularity of competitive video gaming (also known as Esports), Esports analytics has been a field of growing scientific interest in the recent years. Studies discussing player behaviour, skill learning and team performance have been conducted through Multiplayer Online Battle Arena games such as League of Legends. In this paper, we propose a multi-modal approach towards stress response modeling in competitive LoL games. We collect wearable physiological sensor data, mouse & keyboard logs and in-game data in order to study the relationship between player stress responses and in-game behaviour. We discuss the design criteria and propose future studies using the collected dataset.

In this paper, we propose a framework for personalised crisis management training through the use of an applied game. The framework particularly focuses on ubiquitously assessing and manipulating player stress levels during training, and evaluating player performance by providing personalised feedback. To achieve these goals, the framework leverages techniques for multi-modal player modeling through physiological sensors, in-game events and self-report data. Specifically, the present paper (1) discusses design decisions for the personalised crisis management training framework, and (2) presents the game prototype with which user-studies will be performed. Presently, the game prototype is being developed in close collaboration with actual crisis management experts.

The present paper focuses on moral choices in interactive narrative games. Particularly, it investigates factors that contribute to moral engagement in short-term decision making in games; as opposed to the somewhat better understood factors that underlie moral disengagement in games. To this end, the paper proposes factors for assessing moral engagement in games, that build upon (1) the general aggression model, (2) the moral disengagement model, and (3) self-determination theory. The paper reports on two case studies that explore the factors in actual interactive video games; it investigates meaningful choices in the games Life is Strange and The Walking Dead Season 1.

To contribute to the domain of player experience research, this paper presents a new PCG environment with a relatively wide expressive range that builds upon the iconic The Legend of Zelda: A Link to the Past action-RPG game; it contributes by providing the openly-available Procedural Zelda environment for gaming research. The paper presents the design goals and design context of the research environment, and provides a detailed overview of the procedural capabilities of Procedural Zelda, together with its capabilities for data logging, to benefit, e.g., player modelling investigations.

This paper investigates strategies to generate levels for action-adventure games. For this genre, level design is more critical than for rule-driven genres such as simulation or rogue-like role-playing games, for which procedural level generation has been successful in the past. The approach outlined by this article distinguishes between missions and spaces as two separate structures that need to be generated in two individual steps. It discusses the merits of different types of generative grammars for each individual step in the process. Notably, the approach acknowledges that the online generation of levels needs to be tailored strictly to the actual experience of a player. Therefore, the approach incorporates techniques to establish and exploit player models in actual play.

Current approaches to adaptive game AI typically require numerous trials to learn effective behavior (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behavior (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this paper, we discuss an alternative to these current approaches. Our alternative approach to adaptive game AI has as its goal adapting rapidly and reliably to game circumstances. Our approach can be classified in the area of case-based adaptive game AI. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behavior in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial real-time strategy (RTS) game. From our results, we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.