Designing Interaction Freedom via Active Inference

问题：使用先进传感器和机器学习（ML）的交互系统的可靠设计是一个尚未解决的问题。新的传感器可以扩展我们与计算机的交互方式，但仍然很难在不过度限制用户行为的情况下进行设计。人工智能算法可以减少人类的工作量，但在复杂的环境中可能会失败，并且可以控制，去技能和剥夺权力。我们没有原则性的工作流程来设计交互，以允许用户灵活地与支持AI共享自主权。目标：将主动推理理论整合到人机交互回路中，通过传感器和ML/推理嵌入与动态中介机制将人类行为联系起来，在人类和系统之间创建端到端的相互适应回路。开发新颖的交互机制，用于显式和隐式控制AI自治水平，通过共享自治赋予人们权力，同时保持他们的代理。用于计算交互设计的可组合软件工具，其支持与人类耦合的ML注入传感器的原型设计和分析，它可以集成概率因果模型来解决高级传感器的逆问题，适应闭环数据。影响：使用ML让用户自由地表达自己，我们可以对用户异质性保持鲁棒性，确保不同用户的公平性，并实现技术的创造性使用。我们的工具将成为未来使用新型传感器和丰富数据空间的可用接口的基础，并且可以共享和快速构建进步，从而改变HCI研究工作流程。应用：1.通过柔软的可编程电子皮肤进行全手触摸交互，用于具有新颖形式的个性化界面和假肢。2. Google的Soli雷达用于3D手势，姿势和立体交互，3。用于情境感知交互的人类射频感测。

Problem:Reliable design of interactive systems using advanced sensors and machine learning (ML) is an unsolved problem. New sensors could expand how we interact with computers, but are still hard to design for, without overly constraining user behaviour.AI algorithms can reduce human workload, but can fail in complex contexts and can control, deskill and dis-empower people. We have no principled workflows for designing interaction to allow users to flexibly share autonomy with supporting AI.Objectives:Integrate Active Inference theory into the human-computer interaction loop, linking human behaviour via sensors and ML/inference embeddings with dynamic mediating mechanisms to create end-to-end mutually adaptive loops between humans and systems.Develop novel interaction mechanisms for explicit and implicit control of AI autonomy levels to empower people via shared autonomy, while maintaining their agency.Create systematic, composable software tools for computational interaction design which support prototyping and analysis of ML-infused sensors coupled with humans, which can integrate probabilistic causal models to solve inverse problems with advanced sensors, adapting to closed-loop data.Impact:Using ML to give users freedom to express themselves individually, we can be robust to user heterogeneity, ensure fairness for diverse users and enable creative uses of technologies. Our tools will form the foundation of future usable interfaces with novel sensors and rich data spaces, and advances can be shared and rapidly built on, transforming HCI research workflows.Applications:1. Whole hand touch interaction via soft, programmable electronic skin, for personaliseable interfaces with novel forms, and prosthetics. 2. Google's Soli radar for 3D gesture, pose and proxemic interaction, 3. Radio Frequency sensing of humans for context-aware interaction.