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，但在不过度限制用户行为的情况下，仍然很难进行设计。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，并可能控制、削弱人们的能力。我们没有设计交互的原则工作流程，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/推理嵌入将人类行为与动态中介机制联系起来，在人类和系统之间创建端到端的相互适应回路。开发新的交互机制，对人工智能自治水平进行显性和隐性控制，通过共享自治赋予人们权力，同时保持他们的能动性。为计算交互设计创建系统的、可组合的软件工具，支持机器学习注入的传感器与人类耦合的原型设计和分析，可以集成概率因果模型来解决与先进传感器的逆问题，适应闭环数据。影响：使用机器学习让用户可以自由地表达自己，我们可以对用户的异质性保持稳健，确保对不同用户的公平，并实现对技术的创造性使用。我们的工具将成为未来可用的新型传感器和丰富数据空间接口的基础，并且可以共享和快速构建进展，从而改变HCI研究工作流程。通过柔软的、可编程的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. 3.谷歌的Soli雷达用于3D手势，姿势和邻近交互；用于上下文感知交互的人类射频传感。

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.