In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR , an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features reconstructed gameplay sessions, heatmaps for data visualization, a comprehensive annotation system, and advanced tracking for hands, camera, input, and audio, providing in-depth insights for nuanced game analysis. To evaluate GAMR 's effectiveness, we conducted an experimental study with game development students across two game genres: action-adventure and first-person shooter (FPS). The participants used GAMR and provided feedback on its utility. The results showed a significant positive impact of GAMR in both genres, particularly in action-adventure games. This study demonstrates GAMR 's effectiveness in MR game development and suggests its potential to influence future MR game analytics, addressing the specific needs of developers in this evolving area.

Traditional methods of video game bug detection, such as manual testing, have been effective, but they can also be time-consuming and costly. While Automated Bug Detection (ABD) techniques hold great promise for improving testing, they still face several challenges that need to be addressed to be effective in practice. In this work, we introduce a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in games as anomalies. The detected buggy frames are then clustered to determine the category of the occurred bug. The framework was evaluated on two First Person Shooter (FPS) games. We further enhanced the framework by implementing a Reinforcement Learning (RL) agent to autonomously gather datasets, effectively addressing the need for human players to collect data and manually browse through games. The enhancement was performed on a Role-Playing Game (RPG). The outcomes obtained validate the effectiveness of the framework.

We introduced a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in video games as anomalies. The detected buggy frames are then clustered to determine the category of the occurred bug. The framework was evaluated on two First Person Shooter (FPS) games. Results show the effectiveness of the framework.

Emotion detection is a crucial component of Games User Research (GUR), as it allows game developers to gain insights into players’ emotional experiences and tailor their games accordingly. However, detecting emotions in Virtual Reality (VR) games is challenging due to the Head-Mounted Display (HMD) that covers the top part of the player’s face, namely, their eyes and eyebrows, which provide crucial information for recognizing the impression. To tackle this we used a Convolutional Neural Network (CNN) to train a model to predict emotions in full-face images where the eyes and eyebrows are covered. We used the FER2013 dataset, which we modified to cover eyes and eyebrows in images. The model in these images can accurately recognize seven different emotions which are anger, happiness, disgust, fear, impartiality, sadness and surprise.We assessed the model’s performance by testing it on two VR games and using it to detect players’ emotions. We collected self-reported emotion data from the players after the gameplay sessions. We analyzed the data collected from our experiment to understand which emotions players experience during the gameplay. We found that our approach has the potential to enhance gameplay analysis by enabling the detection of players’ emotions in VR games, which can help game developers create more engaging and immersive game experiences.

We present Echo+, an expanded and improved version of our previous gameplay tool, Echo. Echo+ is designed to help bridge the gap between video footage analysis and game analytics. It facilitates this by interactively reconstructing gameplay sessions from recorded data, complete with their original graphics. We present and discuss several new features and changes integrated into Echo+, many of which are based on feedback from our previous evaluation of Echo. We also present a user evaluation that compares the usefulness of Echo+ across four different popular game genres. Participants found Echo+ to be useful in all genres, but there was no clear ordering to them regarding usefulness. We identified several key differences in terms of how Echo+ is used in different genres. In addition, we discuss various avenues for expansion of the tool and possible approaches for evaluating it in the future.

When learning how to develop AI behavior it is common for students to test different ideas before settling on a desired outcome. This functionality is not available in modern behavior tree systems beyond the traditional methods of duplication and conditional execution. We present NCAlt - a visual behavior authoring framework for exploring multiple game AI behaviors. The system also allows selective merging of nodes between multiple behavior tree alternatives and their visual differencing in the game scene and behavior tree views. We present two worked examples which demonstrate how NCAlt can improve the workflow of game development students. We have conducted a user study where NCAlt was compared against NodeCanvas for creating and using alternatives with moderately skilled game development students. Participants rated NCAlt 15% higher than NodeCanvas on the System Usability Scale (SUS). NCAlt was also rated higher on a self-developed questionnaire. We also conducted a semi-structured interview and a detailed longitudinal follow-up and obtained mostly positive feedback for NCAlt. The results suggest NCAlt has a potential to improve the types of workflows with behavior trees that involve exploration for game development students and facilitate learning, as a result. NCAlt was built on top of NodeCanvas - a plugin for the Unity Engine.

Driving simulators play an important role in vehicle research. However, existing virtual reality simulators do not give users a true sense of presence. UniNet is our driving simulator, designed to allow users to interact with and visualize simulated traffic in mixed reality. It is powered by SUMO and Unity. UniNet's modular architecture allows us to investigate interdisciplinary research topics such as vehicular ad-hoc networks, human-computer interaction, and traffic management. We accomplish this by giving users the ability to observe and interact with simulated traffic in a high fidelity driving simulator. We present a user study that subjectively measures user's sense of presence in UniNet. Our findings suggest that our novel mixed reality system does increase this sensation.

Game development is a naturally iterative process where many ideas are tested and prototyped before final decisions are made. Given the increased usage of visual scripting systems in game development recently, it is apparent that these tools must be able to support every aspect of this process. One aspect that is not well captured is the exploration of alternatives. SuBViS was developed as a solution to this problem. It is a visual scripting system for exploring parallel ideas in game development through the use of alternatives at graph and node levels. These two levels of exploring alternatives can be combined or used separately. This paper presents a use case example, which demonstrates how SuBViS can improve workflow and communication between team members. It also discusses a small-scale user study and the results obtained therein. SuBViS was developed on top of Unreal Engine's existing Blueprint Visual Scripting system.

We present a new interface for interactive comparisons of more than two alternative documents in the context of a generative design system that uses generative data-flow networks defined via directed acyclic graphs. To better show differences between such networks, we emphasize added, deleted, (un)changed nodes and edges. We emphasize differences in the output as well as parameters using highlighting and enable post-hoc merging of the state of a parameter across a selected set of alternatives. To minimize visual clutter, we introduce new difference visualizations for selected nodes and alternatives using additive and subtractive encodings, which improve readability and keep visual clutter low. We analyzed similarities in networks from a set of alternative designs produced by architecture students and found that the number of similarities outweighs the differences, which motivates use of subtractive encoding. We ran a user study to evaluate the two main proposed difference visualization encodings and found that they are equally effective.

We present GEM-NI -- a graph-based generative-design tool that supports parallel exploration of alternative designs. Producing alternatives is a key feature of creative work, yet it is not strongly supported in most extant tools. GEM-NI enables various forms of exploration with alternatives such as parallel editing, recalling history, branching, merging, comparing, and Cartesian products of and for alternatives. Further, GEM-NI provides a modal graphical user interface and a design gallery, which both allow designers to control and manage their design exploration. We conducted an exploratory user study followed by in-depth one-on-one interviews with moderately and highly skills participants and obtained positive feedback for the system features, showing that GEM-NI supports creative design work well.