Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters likeCounter-Striketo real-time strategy games such asStarCraft IIand racing games likeGran Turismo. While these achievements are notable, applying these AI methods in commercial video game production remains challenging due to computational constraints. In commercial scenarios, the majority of computational resources are allocated to 3D rendering, leaving limited capacity for AI methods, which often demand high computational power, particularly those relying on pixel-based sensors. Moreover, the gaming industry prioritizes creating human-like behavior in AI agents to enhance player experience, unlike academic models that focus on maximizing game performance. This paper introduces a novel methodology for training neural networks via imitation learning to play a complex, commercial-standard, VALORANT-like 2v2 tactical shooter game, requiring only modest CPU hardware during inference. Our approach leverages an innovative, pixel-free perception architecture using a small set of ray-cast sensors, which capture essential spatial information efficiently. These sensors allow AI to perform competently without the computational overhead of traditional methods. Models are trained to mimic human behavior using supervised learning on human trajectory data, resulting in realistic and engaging AI agents. Human evaluation tests confirm that our AI agents provide human-like gameplay experiences while operating efficiently under computational constraints. This offers a significant advancement in AI model development for tactical shooter games and possibly other genres.

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been extensively discussed in a video game context. Motivated by the lack of a comprehensive review on the ethics of AI as applied to games, we survey the current state of the art in this area and discuss ethical considerations of these systems from the holistic perspective of theaffective loop. Through the components of this loop, we study the ethical challenges that AI faces in video game development.Elicitationhighlights the ethical boundaries of artificially induced emotions;sensingshowcases the trade-off between privacy and safe gaming spaces; anddetection, as utilised during in-gameadaptation, poses challenges to transparency and ownership. This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future. By setting an appropriate framework we aim to protect users and to guide developers towards safer and better experiences for their customers.

Researchers and designers have considered how gamification can make education more effective. In this paper, the role of player-character relationships is considered. Designers can use various factors to get users attached to their character, including the feeling of responsibility. In this paper, we investigate whether feeling responsible for one’s in-game character improved the efficacy of a gamified educational experience. An educational, gamified story called Humanatees was designed to incite the protégé effect to see if participants learned more when they felt responsible for their character. The results suggest that participants in the experimental condition achieved significantly higher learning gains, providing evidence for the protégé effect. However, how this effect is established is more complex than feeling responsible. Future work should shed more light on the dynamics behind the protégé effect.

Increasingly, videogame designers harness game usability techniques to inform design decisions and reduce costs, but advanced techniques are not commonplace yet—perhaps due to failing to see their benefits or a lack of expertise and facilities. In this paper, we present a case study that demonstrates the value of using eye-tracking to help guide the design process of a narrative game by an independent game studio. The designers of this studio had defined four options of how text should be presented to players (Horizontal, Vertical, Subtitle, and Messenger), and were deadlocked on what to choose. As part of a collaboration with a university, a within-subjects eye-tracking study was conducted with 15 participants to evaluate the options. Combining the eye-tracking data with stated user preferences, designers reached a consensus that the game in question benefits from strategies derived from messenger on smart-phone interaction design—later confirmed with statistical analysis of fixation transition on elements of interest on screen. The use of eye-tracking broke the deadlock and helped inform a final design decision, as demonstrated by a decision making impact analysis that describes the design process. The paper ends with a reflection on the applicability of this academia-industry model to other contexts.

Physical and digital games are increasingly used and acknowledged for their cognitive effects, but there is a lack of understanding of how a game’s medium directly impacts cognitive functions. This exploratory case study presents a novel method of using mobile eye-tracking to compare working memory and visual attention between physical and digital versions of the board game Ticket to Ride in natural settings. For visual attention, no significant differences between the versions were measured. However, results indicate that fixation duration is significantly greater while playing the digital version versus the physical version, suggesting the counter-intuitive finding that the digital version increases working memory load. The study design, with evidence for high ecological validity, and its findings provide a foundation for future studies for assessing cognitive processing of game media. This work also provides inspiration for making game media considerations for obtaining specific cognitive experiences and increasing accessibility.

The concept of flow is used extensively in HCI, video games, and many other fields, but its prevalent definition is conceptually vague and alternative interpretations have contributed to ambiguity in the literature. To address this, we use cognitive science theory to expose inconsistencies in flow's prevalent definition, and introduce fuse, a concept related to flow but consistent with cognitive science, and defined as the "fusion of activity-related sensory stimuli and awareness''. Based on this definition, we develop a preliminary model that hypothesizes fuse's underlying cognitive processes. To illustrate the model's practical value, we derive a set of design heuristics that we exemplify in the context of video games. Together, the fuse definition, model and design heuristics form our theoretical framework, and are a product of rethinking flow from a cognitive perspective with the purpose of improving conceptual clarity and theoretical robustness in the literature.

Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open question how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithms such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular, we augment the neighbourhood of a Markov Random Fields approach to take into account not only local but also symmetric positional information. We conduct several empirical tests, including a user study that show the improvements achieved by the proposed modifications and obtain promising results.

The concept of flow is central to many fields including video game design, but the phenomenon is poorly understood scientifically, with no agreement on the cognitive components underlying it. In this paper, we present a cognitive science-based model of the flow experience that puts the processing of information at its core. For this, we identify key theories from the literature and propose two hypotheses about the cognitive processes?the "conditions"?underlying flow's phenomenology. We then describe the mechanisms that facilitate the fulfillment of these conditions and propose a small number of design heuristics for eliciting flow in video games. Finally, we consider some of our model's implications for the traditional interpretation of the phenomenon and discuss the need to test our framework with empirical studies.

Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human playstyles to create an automated playtesting system. The procedural personas are realized through evolving the utility function for the Monte Carlo Tree Search agent. We compare the performance and results of the evolution agents with the standard Vanilla Monte Carlo Tree Search implementation as well as to a random move-selection agent. We then observe the impacts on both the game's design and the game design process. Lastly, a user study is performed to compare the agents to human play traces.

Gamification and serious games have the capacity to increase engagement in often non-engaging contexts such as a test. In this study, we gamified a psychological test, the Thematic Apperception Test (TAT), with the player motivation concepts of achievement, exploration, and social interactions. We used the platform Study-Crafter to implement our games and ran a study to test the effectiveness of this gamified psychological assessment. All participants completed both the standard version of the TAT and our gamified versions and rated their experience in each setting through self-reports. Our results show that the gamified versions of the TAT provided a more enjoyable and motivating experience than the standard version. We conclude that gamifying psychological tests has potential for increasing motivation in psychological assessments while questions of validity remain to be addressed.

Personalization has been explored in the context of games in many forms (e.g., dynamic difficulty adjustment, affective video games, adaptive systems, experience-driven PCG, etc.). The majority of techniques used in these fields have relied on data-driven or manual methods for identifying game components to modify for personalization. We propose a theoretical framework for identifying and categorizing low-level components of games that can be personalized. In this paper we first perform a review of game design frameworks and personalization approaches. We then systematically identify the aspects of games which have been utilized for personalization and which components have been identified in game design frameworks as building blocks of games. We synthesize the identified components into categories of game elements. We then propose PEAS, a theoretical framework for personalization through the adaptation of the Player, Environment, Agents, and System.

This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.

This survey explores procedural content generation via machine learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content, such as sprites and sound effects. In addition to using PCG for autonomous generation, cocreativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory networks, autoencoders, and deep convolutional networks; Markov models: $n$-grams and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small data sets, lack of training data, multilayered learning, style-transfer, parameter tuning, and PCG as a game mechanic.

Jumping has been an important mechanic since its introduction in Donkey Kong. It has taken a variety of forms and shown up in numerous games, with each jump having a different feel. In this paper, we use a modified Nintendo Entertainment System (NES) emulator to semi-automatically run experiments on a large subset (~30%) of NES platform games. We use these experiments to build models of jumps from different developers, series, and games across the history of the console. We then examine these models to gain insights into different forms of jumping and their associated feel.

In this paper we profile the stress responses of patients diagnosed with post-traumatic stress disorder (PTSD) to individual events in the game-based PTSD stress inoculation and exposure virtual environment StartleMart. Thirteen veterans suffering from PTSD play the game while we record their skin conductance. Game logs are used to identify individual events, and continuous decomposition analysis is applied to the skin conductance signals to derive event-related stress responses. The extracted skin conductance features from this analysis are used to profile each individual player in terms of stress response. We observe a large degree of variation across the 13 veterans which further validates the idiosyncratic nature of PTSD physiological manifestations. Further to game data and skin conductance signals we ask PTSD patients to indicate the most stressful event experienced (class-based annotation) and also compare the stress level of all events in a pairwise preference manner (rank-based annotation). We compare the two annotation stress schemes by correlating the self-reports to individual event-based stress manifestations. The self-reports collected through class-based annotation exhibit no correlation to physiological responses, whereas, the pairwise preferences yield significant correlations to all skin conductance features extracted via continuous decomposition analysis. The core findings of the paper suggest that reporting of stress preferences across events yields more reliable data that capture aspects of the stress experienced and that features extracted from skin conductance via continuous decomposition analysis offer appropriate predictors of stress manifestation across PTSD patients.

Computer games have recently shown promise as a diagnostic and treatment tool for psychiatric rehabilitation. This paper examines the positive impact of affect detection and advanced game technology on the treatment of mental diagnoses such as Post Traumatic Stress Disorder (PTSD). For that purpose, we couple game design and game technology with stress detection for the automatic profiling and the personalized treatment of PTSD via game-based exposure therapy and stress inoculation training. The PTSD treatment game we designed forces the player to go through various stressful experiences while a stress detection mechanism profiles the severity and type of PTSD via skin conductance responses to those in-game stress elicitors. The initial study and analysis of 14 PTSD-diagnosed veteran soldiers presented in this paper reveals clear correspondence between diagnostic standard measures of PTSD severity and skin conductance responses. Significant correlations between physiological responses and subjective evaluations of the stressfulness of experiences, represented as pair wise preferences, are also found. We conclude that this supports the use of the simulation as a relevant treatment tool for stress inoculation training. This points to future avenues of research toward discerning between degrees and types of PTSD using game-based diagnostic and treatment tools.