Level design is one of the most labor-intensive processes in video game development – especially because communicating a creative vision across designers, artists, writers, and gameplay engineers requires extensive iterative refinement. Traditionally, this involves rounds of white boxing and set dressing, which distributes labor effectively but limits rapid exploration and creative experimentation. Recent advances in AI-driven image generation offer timely opportunities to transform these workflows, but risk losing control to the model’s interpretation and capabilities. We investigate the impact of generative AI on designers’ control, expressiveness, and efficiency through a mixed-methods study (n=20), comparing drawing (full control), text-to-image (full AI), and our approach: a visualization pipeline combining generative AI with user-centered spatial control. It succeeded in enhancing visual expressiveness and sense of control over bare AI, matched manual drawing (without necessitating advanced skills), and enabled faster iteration – highlighting the potential of giving back control to creative visionaries.

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 modern online multiplayer video games, players experience a plethora of social interactions like communication, competition, and completion of shared goals. Those experiences are, however, hindered by toxic behavior. Game developers have sought to counteract such behaviors by introducing what we call Behavior Ranking Systems, i.e., systems that track and assess player in-game behavior, based on which players are assigned ranks and receive rewards or punishments accordingly. Despite their important role, information on the functionality of these systems is difficult to find, limited in detail, and often outdated. In this paper, we provide a review of four prominent Behavior Ranking Systems, analyze their designs in detail, and identify their features. Based on our review, we define a design space encompassing the core features of a Behavior Ranking System to inform the design of new such systems.

Internal game balancing is one of the major components that affect player experience, as it is responsible for a large share of development time, the majority of game update patches and long-term player satisfaction. This makes tools and methodologies of assessing and advancing game balance a valuable endeavor for industry and academia. During the past decades, scientific research produced numerous outputs to inform and enhance game balancing, yet most of them only adhere to a single dimension of balance: fixed (end-game) scenarios. However, games are usually experienced throughout a continuous spectrum of ever-changing constellations, which should be reflected. Using simulation, game-playing AI, visual analytics and informative metrics, we introduce a methodology and implementation of Progression Balancing, incorporating multi-dimensional game aspects. For the sake of exposition and ecological validity, we applied it in one of the most successful recent games (Baldur's Gate 3), and evaluated its efficacy with help of its player community.

The video game industry deals with a fast-paced, competitive and almost unpredictable market. Trends of genres, settings and modalities change on a perpetual basis, studios are often one big hit or miss away from surviving or perishing, and hitting the pulse of the time has become one of the greatest challenges for industrials, investors and other stakeholders. In this work, we aim to support the understanding of video game trends over time based on data-driven analysis, visualization and interpretation of Steam tag evolutions. We confirm underlying groundwork that trends can be categorized in short-lived fads, contemporary fashions, or stable classics, and derived that the surge of a trend averages at about four years in the realm of video games. After using industrial experts to validate our findings, we deliver visualizations, insights and an open approach of deciphering shifts in video game trends.

Figure 1: Clusters of countries with similar cultural attitudes based on two different sets of metrics and algorithms.1a (left) uses mean values and hierarchical clustering.1b (right) uses cosine similarity and k-means clustering.

The concept of dealing damage is established and widespread in video games. With growing complexity and countless interactions in modern games, capturing how damage unfolds becomes an intricate problem - for developers just as for players. Misunderstanding how to optimize damage potentials includes risks of game imbalances, game-breaking exploits, mismatches between player skill and challenge (harming flow), and impaired perceived competence. All of these considerably impact player experience, game reception, success, and retention, yet polishing optimal strategies remains often a player community effort. To accelerate, inform and ease this process, we implemented an interactive tool capable of simulating, visualizing, planning and comparing damage strategies in video games. Following a case study within the Guild Wars 2 community, we contribute a player-driven perspective on the problem of damage optimization, as well as an artifact that resulted in empirical improvements – advancing the fields of game analytics, game evaluation methods and self-regulated learning.

Online multiplayer games are getting increasingly popular and complex at the same time, while acknowledging adequate individual contributions to the success or failure of a session remains a formidable challenge. Yet, explainable and tangible insights are a necessity for players’ self-improvement, understanding one’s own performance can diminish frustration and toxicity, and properly quantifying proficiency yields promising potential in overcoming balancing as well as matchmaking issues. Motivated by this series of use cases, we set out to conduct an ecologically valid co-design study with players of the MMORPG Guild Wars 2, implement visualizations and measures drawing from a rich endgame dataset, and derive simple but intricate guidelines to consider when quantifying individual player performance.

With video games and Esports becoming ever-present, playing as long as possible to improve proficiency has become a common strategy among video game enthusiasts. This behavior does not only bear increased health and social risks, but is also connected to performance decline, which itself inhibits skill acquisition. To promote health and advance self-regulated learning, we investigate the effects of distributed practice on skill acquisition in an ecologically valid Esports task. As research about distributed practice in video games is restricted to simple games or short practice sessions, we explore the impact of distributed versus massed practice with an extended training period of 45 minutes. Using (n = 16) participants and a complex contemporary fighting game, we show that massed practice schedules do not provide any advantage over distributed ones - suggesting that practice time can be evenly distributed without improvement losses, which would alleviate health risks connected to longer sessions.

Within the field of game analytics, visualization has been widely instrumentalized to provide an effective graphical interface for analysts to understand player experiences, preferences, and behaviors. However, it is often difficult to decipher strategies or intent with vast amounts of granular events that are rapidly accumulating. Most current visualization approaches focus on aggregated data, utilizing fundamental charts, heatmaps, and scatterplots to conduct statistical analyses, neglecting sequential information across player behavior. In this paper, we focus on sequential visualization approaches, proposing a set of requirements and an initial version of a visualization system developed based on a participatory, iterative co-design approach, collaborating with stakeholders of different games. This co-creative development demonstrates what features analysts need to reason about process visualization, which informs game analytics research. We further discuss a technical realization incorporating the resulting requirements, e.g., manipulation, filtering, abstraction, and segmentation, as well as the underlying algorithms used to realize them.

With the rise of popularity and societal importance of video games, trends of grinding play for excessive amounts of time to improve proficiency becomes more and more common. This bears not only physical and mental health risks, but has already shown to be detrimental for skill acquisition, defeating its purpose in the first place. For the sake of health promotion, well-being, and the advancement of self-regulated learning, we specifically investigate the impact of detailed feedback on player performance. As research about comparable activities in related domains suggest inconclusive implications for when feedback in video game learning should be presented, we particularly explore the impact of delivering immediate versus delayed feedback, during or after play. Using (n = 30) participants and the well-investigated Super Mario Bros., we communicate insights in favor for deploying feedback immediately when polishing one's video game related skills such as in speedrunning.

Self-reflection, a process consisting of evaluation, attribution, and adaptation, is recognized as key to learning in esports games. To better facilitate self-reflection, many games and third-party tools feature retrospective visualizations that provide players with details about their performance. However, previous work suggested that existing visualizations, which typically aggregate data, do not present sufficient information to players. In this work, we explore the impact of process visualizations, which present gameplay as a sequence of actions, on self-reflection and performance in a League of Legends training exercise through a mixed-methods study. Our results found that players who reflected using a process visualization significantly improved their performance and demonstrated significant differences in how they evaluated themselves compared to those who reflected using an aggregate visualization. We discuss what these results mean in relation to self-reflection and learning in esports and how they impact the future use of process visualizations in games.

Today's videogames are often intertwined with profound religious narratives, content, characters, and artifacts - but how do player communities understand the role of religion in games, and what is the impression of believers on the use, portrayal, and representation of the religion they practice? This paper dives deep into this confluence by rigorously analyzing discussions from religion and videogame subreddits that often depict the most popular communication channel for their communities. Following topic modeling, we cluster posts and comments from the three major religious groups and the most popular related videogame subreddits, bringing forth representative cultural sentiments and patterns of players and religious stakeholders. Through this, we discovered 22 distinct sub-themes, further categorized into the three overarching themes of (a) Blasphemous Elements of Videogames, (b) Religion as a Design Space for Videogames, and (c) Videogames for Religious Education and Community Forming.

Video game analytics are widely adopted within academia and industrial development – yet, the player’s perspective is rarely considered as a driving factor, even though vast communities are interested in self-regulated learning, cross-comparisons and empirical trends or insights. In this work, we propose to empower the role of the player as the central impulse for analytics, collect requirements and data from the community of one of the most popular MMORPGs (Guild Wars 2), establish a tool over a 18-month period of participatory development and discuss the co-created features. With analytics for in-game logs from (n = 175, 099) unique players and atomic game actions of over 2 million hours played, we contribute a large-scale, long-term iterative implementation and evaluation of a player-driven instrument to quantify popularity, viability and hierarchical inspection between classes or individual performances. This tool found frequent usage among the actual game community, delivering game data science to the very player.

Identifying explainable player strategies and decision patterns that give insights into player behavior is one of the most difficult tasks for game analytics, yet yields great informative potential for various purposes. Industrial stakeholders can capture player experience and infer issues or feedback on design, content, and game balancing - while players themselves might want to leverage this technique to contrast their style of play to other players, fostering self-regulated learning. On top of that, in the case of educational games, the identification of learning strategies (as well as the discovery of popular erroneous strategies) could even elevate their potential to successfully communicate educational concepts. To advance this field, we investigate how the spatial map of a game contributes to identifying player strategies and emphasize the importance of the appropriate level of abstraction to capture strategical decisions. Using visualizations and expert domain knowledge about spatial abstraction, we illustrate how the partitioning into affordance zones can reveal patterns and strategies in gameplay. To showcase the generalizability of our methodology, we investigate two case studies for the distinct genres of educational games (Parallel) and MMORPGs (Guild Wars 2). The two case studies unveil insightful strategies between different player sets of interest – only possible by the apt level of spatial abstraction.

Within the field of games, visualization of player log data is becoming an important method for its utility in providing an intuitive and informative way to understand players' experience, which is thus often used by game analytics personnel and game user researchers. Moreover, even players themselves show increased interest in using analytics to quantify and self-improve their performances. Among other types of visualizations, node-edge graphs have proven to be capable of revealing the process of individual and aggregated players, allowing analysts to discover play patterns that can inform game design. However, visualization of player traces often has several disadvantages. First, displaying players' process data does not trivially scale, as tendentially high variance often leads to complex and abstruse graphs. Second, when aggregating all players, individual variations are often overlooked. For example, data from minorities (e.g., casual players or players who played the game very differently than others) are often treated as outliers or noise. In this paper, we present an iterative segmentation approach that allows analysts to interact with the visualization and group players into different subcategories through meta-data or behavioral patterns. Using this approach, analysts can bypass complicated visualizations while protecting significant unique information.

Existing Data Acquisition (DAQ) systems are not suitable for increased data rates of currently proposed 6G systems. They support only unidirectional communication, cannot store large amounts of data, require resource-intensive FPGA implementations due to use of complex transmission protocols or focus only on FPGA-to-FPGA connections. In our work, we therefore introduce a DAQ system based on RFSoC FPGAs and commodity server hardware, using plain Ethernet as communication protocol to reduce complexity and overhead.

Emotion regulation and coping strategies are key to resilience, problem solving and eventually well-being in everyday life, but investigating or influencing these without adjustable and ecologically valid environments still poses a major challenge. With respect to video games, stressful events can frequently appear and, depending on the individual’s emotion regulation, lead to frustrating experiences, dissatisfied players or even churn – or to a sense of accomplishment, mastery or positive tension. In the greater endeavor of establishing games for studying, controlling and reinforcing resilience, we developed an alternate reality game that unifies the advantages of a fully customizable game environment with the close connection to real-life interactions. To estimate how emotion regulation strategies could be initiated as well as investigated, this work first induces different kinds of established stressors (time pressure, social encounters and being unchangeably stuck) into ordinary gameplay, quantifies the players’ physiological and psychological responses and addresses the participants’ strategies on dealing with these situations. In this paper we present results of the study showing that the induced stressors were effective in constituting stressful situations comparable to real-life experiences; and each of these conditions resulted in different types of physiological and perceived responses and behaviors, e.g. continuous stress in social encounters versus instantaneous stress when notified about time pressure; or frustrating experiences when being forcibly stuck versus stress that participants underwent but were able to work against. With this platform to study how people cope with stress, instituted within a multi-modal mixed-methods evaluation, we contribute to games beyond entertainment towards educating resilience.

Anthropology, defined as the scientific study of human development and societies, can play a critical role in the design of (video) game mechanics and aesthetics. Especially religion and culture are seen as fundamental components in building communities, societies, and human evolution. In this paper, we present (1) a case study using UX methods such as ethnography, grounded theory, participatory design, and user interviews to develop and evaluate design spaces generated from cultural spaces, (2) this design space in the form of paradigms and syntagms and (3) how this design space can assist in the construction of novel and culturally sensitive characters.

University courses for System-on-Chip (SoC) design mostly focus on particular aspects. Whereas this can provide detailed understanding of these aspects, it neglects system integration specific topics such as crossing digital and analog domains. In addition, many courses skip practical issues and do not teach Electronic Design Automation (EDA) tools. This is reasonable in the context of a specialized course, but omitting these techniques often prevents students from making active use of the learned knowledge in their own projects. In the following, we present our technological platform to teach SoC design in a holistic lab course: The course takes students from writing the first line of Verilog code to advanced digital and analog design. It introduces simulation of digital and analog systems, debugging methods for software and hardware, CPU bus architecture, custom peripherals and driver development. This work is prototyped using Field Programmable Gate Arrays (FPGAs) and later transferred to an ASIC target, covering standard cell synthesis and analog layout. At the end of the semester, students finalize the project with technical documentation writing. The course is built on the design of an audio peripheral, combining all topics in a single real-world system. It enables students to apply theoretical aspects from various lectures in the SoC curriculum in practice and equips them with the skills needed to dive deeper into each of the involved topics on their own.

Video game testing has become a major investment of time, labor, and expense in the game industry. Particularly the balancing of in-game units, characters, and classes can cause long-lasting issues that persist years after a game's launch. While approaches incorporating artificial intelligence have already shown successes in reducing manual effort and enhancing game development processes, most of these draw on heuristic, generalized, or optimal behavior routines, while actual low-level decisions from individual players and their resulting playing styles are rarely considered. In this article, we applydeep player behavior modelingto turn atomic actions of 213 players from six months of single-player instances within the MMORPG Aion into generative models that capture and reproduce particular playing strategies. In a subsequent simulation, the resulting generative agents (“replicants”) were tested against common NPC opponent types of MMORPGs that iteratively increased in difficulty, respective to the primary factor that constitutes this enemy type (Melee, Ranged, Rogue, Buffer, Debuffer, Healer, Tank, or Group). As a result, imbalances between classes as well as strengths and weaknesses regarding particular combat challenges could be identified and regulated automatically.

Advances in speech recognition, language processing and natural interaction have led to an increased industrial and academic interest. While the robustness and usability of such systems are steadily increasing, speech-based systems are still susceptible to recognition errors. This makes intelligent error handling of utmost importance for the success of those systems. In this work, we integrated anticipatory error handling for a voice-controlled video game where the game would perform a locally optimized action in respect to goal completion and obstacle avoidance, when a command is not recognized. We evaluated the user experience of our approach versus traditional, repetition-based error handling (). Our results indicate that implementing anticipatory error handling can improve the usability of a system, if it follows the intention of the user. Otherwise, it impairs the user experience, even when deciding for technically optimal decisions.

Reconfigurable FETs (RFETs) are ambipolar transistors featuring the ability to conduct both electrons and holes, which is often achieved through the use of silicon nanowires or similar gate-all-around topologies. In this article, we present initial results for standard cell synthesis based on our planar RFET device, featuring top-down planar silicon based technology, lower fabrication complexity than nanowire approaches and a high operating temperature robustness. We first introduce the device physics by explaining the structure and the operating principle on device level. We also summarize recent device optimizations to increase drive current and achieve symmetry between N- and P-type conduction. Next to CMOS-style standard cells, we present a reduced transistor count XOR cell and analyze timing. Transient simulations are performed entirely in TCAD to accurately show device performance. Further we describe extraction of relevant parameters of these circuits for usage in synthesis tools and compare our standard cells to a similar 180nmSOI technology. Afterwards we perform timing analysis for a full adder and explore the boundaries of our device with a larger cryptographic accelerator core.

Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.

Balancing games and producing content that remains interesting and challenging is a main cost factor in the design and maintenance of games. Dynamic difficulty adjustments (DDA) can successfully tune challenge levels to player abilities, but when implemented with classic heuristic parameter tuning (HPT) often turns out to be very noticeable, e.g. as "rubber-banding". Deep learning techniques can be employed for deep player behavior modeling (DPBM), enabling more complex adaptivity, but effects over frequent and longer-lasting game engagements, as well as how it compares to HPT has not been empirically investigated. We present a situated study of the effects of DDA via DPBM as compared to HPT on intrinsic motivation, perceived challenge and player motivation in a real-world MMORPG. The results indicate that DPBM can lead to significant improvements in intrinsic motivation and players prefer game experience episodes featuring DPBM over experience episodes with classic difficulty management.

Balancing the options available to players in a way that ensures rich variety and viability is a vital factor for the success of any video game, and particularly competitive multiplayer games. Traditionally, this balancing act requires extensive periods of expert analysis, play testing and debates. While automated gameplay is able to predict outcomes of parameter changes, current approaches mainly rely on heuristic or optimal strategies to generate agent behavior. In this paper, we demonstrate the use of deep player behavior models to represent a player population (n = 213) of the massively multiplayer online role-playing game Aion, which are used, in turn, to generate individual agent behaviors. Results demonstrate significant balance differences in opposing enemy encounters and show how these can be regulated. Moreover, the analytic methods proposed are applied to identify the balance relationships between classes when fighting against each other, reflecting the original developers' design.