Similarity estimation is essential for many game AI applications, from the procedural generation of distinct assets to automated exploration with game-playing agents. While similarity metrics often substitute human evaluation, their alignment with our judgement is unclear. Consequently, the result of their application can fail human expectations, leading to e.g. unappreciated content or unbelievable agent behaviour. We alleviate this gap through a multi-factorial study of two tile-based games in two representations, where participants (N=456) judged the similarity of level triplets. Based on this data, we construct domain-specific perceptual spaces, encoding similarity-relevant attributes. We compare 12 metrics to these spaces and evaluate their approximation quality through several quantitative lenses. Moreover, we conduct a qualitative labelling study to identify the features underlying the human similarity judgement in this popular genre. Our findings inform the selection of existing metrics and highlight requirements for the design of new similarity metrics benefiting game development and research.
https://doi.org/10.1145/3613904.3642077
Facial emotion recognition (FER) constitutes an essential social skill for both humans and machines to interact with others. To this end, computer interfaces serve as valuable tools for training individuals to improve FER abilities, while also serving as tools for gathering labels to train FER machine learning datasets. However, existing tools have limitations on the scope and methods of training non-clinical populations and also on collecting labels for machines. In this study, we introduce Find the Bot!, an integrated game that effectively engages the general population to support not only human FER learning on spontaneous expressions but also the collection of reliable judgment-based labels. We incorporated design guidelines from gamification, education, and crowdsourcing literature to engage and motivate players. Our evaluation (N=59) shows that the game encourages players to learn emotional social norms on perceived facial expressions with a high agreement rate, facilitating effective FER learning and reliable label collection all while enjoying gameplay.
https://doi.org/10.1145/3613904.3642880
Failure is an integral element of most games, and while some players may benefit from external support, such as cheat codes, to prompt self-soothing, most games lack supportive elements. We asked participants (N=88) to play Anno 1404 in single-player mode, and presented a money-generating cheat code in a challenging situation, also measuring the personality trait of action-state orientation, which explains differences in self-regulation ability (i.e., self-soothing) in response to threats of failure. Individuals higher in state orientation were more likely to take the offer, and used the cheat code more frequently. The cheat code also acted as an external support, as differences in experienced pressure between action- and state-oriented participants vanished when it was used. We found no negative consequences of using external support in intrinsic motivation, needs satisfaction, flow, or performance. We argue that external support mechanisms can help state-oriented players to self-regulate in gaming, when faced with failure.
https://doi.org/10.1145/3613904.3642603
Educational games have emerged as potent tools for helping students understand complex concepts and are now ubiquitous in global classrooms, amassing vast data. However, there is a notable gap in research concerning the effective visualization of this data to serve two key functions: (a) guiding students in reflecting upon their game-based learning and (b) aiding them in analyzing peer strategies. In this paper, we engage educators, students, and researchers as essential stakeholders. Taking a Design-Based Research (DBR) approach, we incorporate UX design methods to develop an innovative visualization system that helps players learn through gaining insights from their own and peers' gameplay and strategies.
https://doi.org/10.1145/3613904.3642484