🤖 AI Summary
A lack of systematic, actionable metrics for evaluating scene diversity in games hinders the development of dynamic game worlds and the enhancement of player experience. This study introduces the first interdisciplinary framework for assessing scene diversity, integrating procedural content generation (PCG), multi-agent simulation, affective computing, and psychological experimental paradigms to establish a unified taxonomy and quantitative measurement model. It bridges the evaluation gap between theoretical research and industrial practice through novel methodological integration. We develop a reusable diversity assessment toolkit and evidence-informed design guidelines. The framework enables game developers to efficiently construct dynamic game worlds that are immersive, educationally effective, and inclusive. Moreover, it provides a foundational methodology for advancing game AI and human–computer interaction research. (124 words)
📝 Abstract
This survey comprehensively reviews the multi-dimensionality of game scenario diversity, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios. By traversing a wide array of disciplines, from affective modeling and multi-agent systems to psychological studies, our research underscores the importance of diverse game scenarios in gameplay and education. Through a taxonomy of diversity metrics and evaluation methods, we aim to bridge the current gaps in literature and practice, offering insights into effective strategies for measuring and integrating diversity in game scenarios. Our analysis highlights the necessity for a unified taxonomy to aid developers and researchers in crafting more engaging and varied game worlds. This survey not only charts a path for future research in diverse game scenarios but also serves as a handbook for industry practitioners seeking to leverage diversity as a key component of game design and development.