We explore the use of Active Inference (AIF) as a computational user model for spatial pointing, a key problem in Human-Computer Interaction (HCI). We present an AIF agent with continuous state, action, and observation spaces, performing one-dimensional mouse pointing and clicking. We use a simple underlying dynamic system to model the mouse cursor dynamics with realistic perceptual delay. In contrast to previous optimal feedback control-based models, the agent's actions are selected by minimizing Expected Free Energy, solely based on preference distributions over percepts, such as observing clicking a button correctly. Our results show that the agent creates plausible pointing movements and clicks when the cursor is over the target, with similar end-point variance to human users. In contrast to other models of pointing, we incorporate fully probabilistic, predictive delay compensation into the agent. The agent shows distinct behaviour for differing target difficulties without the need to retune system parameters, as done in other approaches. We discuss the simulation results and emphasize the challenges in identifying the correct configuration of an AIF agent interacting with continuous systems.

翻译：本文探讨了将主动推理作为计算用户模型应用于空间指向任务——人机交互中的一个关键问题。我们提出了一种具有连续状态、动作和观测空间的主动推理智能体，用于执行一维鼠标指向与点击操作。该模型采用简单的底层动态系统来模拟鼠标光标运动，并引入了符合实际感知的延迟。与以往基于最优反馈控制的模型不同，该智能体通过最小化期望自由能来选择动作，其决策完全基于对感知结果的偏好分布（例如正确点击按钮的观测概率）。实验结果表明，当光标位于目标上方时，智能体能够生成符合人类行为特征的指向运动与点击动作，其终点位置方差与真实用户数据相近。相较于其他指向模型，本工作将完全概率化的预测性延迟补偿机制整合到智能体中。该智能体能够针对不同难度目标表现出差异化行为，而无需像其他方法那样重新调整系统参数。我们讨论了仿真结果，并重点阐述了在与连续系统交互时确定主动推理智能体正确配置所面临的挑战。