用主动推理框架理解人机交互中的行为与适应机制。
Active Inference and Human--Computer Interaction
- 基于生成模型的主动推理，统一建模用户、环境与界面的动态互动。
- 可解释人机交互中的行为，支持实时自适应与离线系统设计。
- 适合研究人机交互理论、智能系统设计者及AI集成开发者。
主动推理是一种基于闭环计算理论的行为理解框架，其核心是拥有内部概率生成模型的代理，该模型编码了对环境中隐藏状态如何引发感官输入的信念。本文综述主动推理及其在建模人机交互循环中的应用潜力。该框架能统一管理对用户、环境、传感器和界面组件的生成模型，既支持离线系统设计，也支持在线实时适应。它为HCI中观察到的行为提供模型化解释，并引入新工具以量化代理感与参与度等关键概念。主动推理为构建人机交互理论提供了新基础，为复杂传感系统的设计提供工具，并推动人工智能技术的整合，使其能够应对用户多样性与情境变化。文章还讨论了实现此类系统面临的实际挑战。
原文摘要 · Abstract (English)
Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human-computer interaction loop. Active Inference provides a coherent framework for managing generative models of humans, their environments, sensors and interface components. It informs off-line design and supports real-time, online adaptation. It provides model-based explanations for behaviours observed in HCI, and new tools to measure important concepts such as agency and engagement. We discuss how Active Inference offers a new basis for a theory of interaction in HCI, tools for design of modern, complex sensor-based systems, and integration of artificial intelligence technologies, enabling it to cope with diversity in human users and contexts. We discuss the practical challenges in implementing such Active Inference-based systems.
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