Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for instance, such AI models can supposedly learn to detect player actions across different games. Despite recent advancements in AI, domain generalisation for modelling the users' experience remains largely unexplored. While video games present unique challenges and opportunities for the analysis of user experience -- due to their dynamic and rich contextual nature -- modelling such experiences is limited by generally small datasets. As a result, conventional modelling methods often struggle to bridge the domain gap between users and games due to their reliance on large labelled training data and assumptions of common distributions of user experience. In this paper, we tackle this challenge by introducing a framework that decomposes the general domain-agnostic modelling of user experience into several domain-specific and game-dependent tasks that can be solved via few-shot learning. We test our framework on a variation of the publicly available GameVibe corpus, designed specifically to test a model's ability to predict user engagement across different first-person shooter games. Our findings demonstrate the superior performance of few-shot learners over traditional modelling methods and thus showcase the potential of few-shot learning for robust experience modelling in video games and beyond.

翻译：领域泛化旨在学习能够在特定任务的不同领域中保持高性能的人工智能（AI）模型。以视频游戏为例，此类AI模型理论上可学习识别不同游戏中的玩家行为。尽管AI领域近期取得了进展，但面向用户体验建模的领域泛化研究仍处于探索阶段。视频游戏因其动态且丰富的上下文特性，为用户体验分析带来了独特的挑战与机遇，但此类体验建模通常受限于规模较小的数据集。因此，传统建模方法因依赖大量标注训练数据并假设用户体验服从共同分布，往往难以弥合用户与游戏之间的领域差异。本文提出一种创新框架应对这一挑战：将领域无关的通用用户体验建模分解为多个领域特定且依赖游戏的任务，并通过少样本学习求解。我们在专门用于测试模型跨不同第一人称射击游戏预测用户参与度能力的公开数据集GameVibe变体上验证了该框架。实验结果表明，少样本学习器相比传统建模方法具有显著性能优势，从而证明了少样本学习在视频游戏及其他领域实现鲁棒性体验建模的潜力。