Player uncertainty in navigation games and its applications

阿尔茨海默病等进行性痴呆症的治疗具有挑战性，部分原因是诊断延迟。在这里，我建议开发一个基于导航的游戏中玩家行为的模型，以检测认知障碍的早期阶段。我将通过采用基于视觉同时定位和映射的贝叶斯理想观察者模型来实现这一点。该模型将拟合和预测玩家在给定第一人称视角和游戏环境地图的情况下的每一时刻的移动决定。我将把模型与正常玩家和阿尔茨海默氏症患者玩游戏的轨迹相匹配，这将产生指示每个玩家的视觉、运动和记忆噪音水平的参数。然后，我将研究什么参数组合最能将患者与正常玩家区分开来。早期诊断将导致早期治疗，从而提高患者的生活质量。此外，这项研究有可能通过保持玩家的参与度来提高游戏体验。

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.