Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have not been thoroughly explored. Advances in this field could anticipate data modelling and analysis, as well as speed up the development process. To try to fill this gap in the literature, we propose a simulator architecture for generating probabilistic synthetic data for serious games based on interactive narratives. This architecture is designed to be generic and modular so that it can be used by other researchers on similar problems. To simulate the interaction of synthetic players with questions, we use a cognitive testing model based on the Item Response Theory framework. We also show how probabilistic graphical models (in particular Bayesian networks) can be used to introduce expert knowledge and external data into the simulation. Finally, we apply the proposed architecture and methods in a use case of a serious game focused on cyberbullying. We perform Bayesian inference experiments using a hierarchical model to demonstrate the identifiability and robustness of the generated data.

翻译：合成数据生成是近年来一个不断发展的研究领域，然而其在严肃游戏中的潜在应用尚未得到充分探索。该领域的进展有望预先推动数据建模与分析，并加速开发进程。为尝试填补文献中的这一空白，我们提出了一种基于互动叙事的严肃游戏概率合成数据生成模拟器架构。该架构设计为通用且模块化，以便其他研究人员能将其应用于类似问题。为模拟合成玩家与问题的互动，我们采用基于项目反应理论框架的认知测试模型。同时，我们展示了概率图模型（特别是贝叶斯网络）如何用于将专家知识与外部数据引入模拟。最后，我们将所提出的架构与方法应用于一个聚焦网络欺凌的严肃游戏案例中，并采用层次模型进行贝叶斯推断实验，以证明生成数据的可识别性与稳健性。