Player uncertainty in navigation games and its applications

导航游戏中玩家的不确定性及其应用

• 批准号: 2890048
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890048
• 关键词: Player; uncertainty; navigation; games; its

Treatment of progressive dementia such as Alzheimer's disease is challenging partly due to delay in diagnosis. Here, I propose developing a model of the players' behaviour in navigation-based games to detect early stages of cognitive impairment. I will do so by adapting a Bayesian ideal-observer model based on visual simultaneous localisation and mapping. The model will fit and predict the player's moment-by-moment movement decisions given the first-person view and the map of the game environment. I will fit the model to the trajectory of normal players and Alzheimer's patients playing the game, which will yield parameters that indicate the levels of visual, motor, and memory noise of each individual player. I will then examine what combination of parameters can best differentiate patients from normal players. Early diagnosis would lead to early treatment, thereby improving the quality of life for patients. Moreover, the research has the potential to enhance gameplay experience by keeping the players engaged.

治疗进行性痴呆（如阿尔茨海默病）具有挑战性，部分原因是诊断延误。在这里，我建议在基于导航的游戏中开发一个玩家行为模型，以检测认知障碍的早期阶段。我将通过采用基于视觉同步定位和映射的贝叶斯理想观察者模型来做到这一点。该模型将根据第一人称视角和游戏环境的地图，拟合并预测玩家每时每刻的移动决策。我将把这个模型与正常玩家和阿尔茨海默病患者玩游戏的轨迹相匹配，这将产生参数，表明每个玩家的视觉、运动和记忆噪音的水平。然后，我将检查哪些参数组合可以最好地区分患者和正常球员。早期诊断有助于早期治疗，从而提高患者的生活质量。此外，该研究有可能通过保持玩家粘性来增强游戏体验。